R Notebook
library(here)here() starts at C:/Users/plancha/exoplanetslibrary(tidyverse)── Attaching core tidyverse packages ────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.4 ── Conflicts ──────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorslibrary(conflicted)
# library(easystats)
exoplanets <- read_csv(here("data", "exoplanet_catalog_080325.csv"))Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
dat <- vroom(...)
problems(dat)Rows: 7418 Columns: 98── Column specification ──────────────────────────────────────────────────────
Delimiter: ","
chr (12): name, planet_status, publication, detection_type, mass_measurem...
dbl (83): mass, mass_error_min, mass_error_max, mass_sini, mass_sini_erro...
lgl (2): hot_point_lon, star_magnetic_field
date (1): updated
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.exoplanetslibrary(skimr)
skim(exoplanets)Warning: There was 1 warning in `dplyr::summarize()`.
ℹ In argument: `dplyr::across(tidyselect::any_of(variable_names),
mangled_skimmers$funs)`.
ℹ In group 0: .
Caused by warning:
! There was 1 warning in `dplyr::summarize()`.
ℹ In argument: `dplyr::across(tidyselect::any_of(variable_names),
mangled_skimmers$funs)`.
Caused by warning in `inline_hist()`:
! Variable contains Inf or -Inf value(s) that were converted to NA.── Data Summary ────────────────────────
Values
Name exoplanets
Number of rows 7418
Number of columns 98
_______________________
Column type frequency:
character 12
Date 1
logical 2
numeric 83
________________________
Group variables None library(tidymodels)── Attaching packages ──────────────────────────────────── tidymodels 1.3.0 ──
✔ broom 1.0.7 ✔ rsample 1.2.1
✔ dials 1.4.0 ✔ tune 1.3.0
✔ infer 1.0.7 ✔ workflows 1.2.0
✔ modeldata 1.4.0 ✔ workflowsets 1.1.0
✔ parsnip 1.3.1 ✔ yardstick 1.3.2
✔ recipes 1.1.1 glimpse(exoplanets)Rows: 7,418
Columns: 98
$ name <chr> "109 Psc b", "112 Psc b", "112 Psc c", "1…
$ planet_status <chr> "Confirmed", "Confirmed", "Confirmed", "C…
$ mass <dbl> 5.743, NA, 9.866, NA, NA, NA, 8.500, 7.10…
$ mass_error_min <dbl> 0.28900, 0.00500, 1.78100, 1.53491, 1.100…
$ mass_error_max <dbl> 1.01100, 0.00400, 3.19000, 1.53491, 1.100…
$ mass_sini <dbl> 6.3830, 0.0330, NA, 16.1284, 11.0873, 4.6…
$ mass_sini_error_min <dbl> 0.07800, 0.00500, NA, 1.53491, 1.10000, 0…
$ mass_sini_error_max <dbl> 0.07800, 0.00400, NA, 1.53491, 1.10000, 0…
$ radius <dbl> 1.152, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ radius_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ radius_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ orbital_period <dbl> 1075.4000, 4.4000, 36336.7000, 326.0300, …
$ orbital_period_error_min <dbl> 0.7000, 0.0004, 6039.4000, 0.3200, 3.2500…
$ orbital_period_error_max <dbl> 0.8000, 0.0002, 9726.0000, 0.3200, 3.2500…
$ semi_major_axis <dbl> 2.051, 0.054, 22.210, 1.290, 1.540, 0.830…
$ semi_major_axis_error_min <dbl> 0.087, 0.002, 2.766, 0.050, 0.070, NA, 0.…
$ semi_major_axis_error_max <dbl> 0.079, 0.002, 3.866, 0.050, 0.070, NA, 0.…
$ eccentricity <dbl> 0.104, 0.376, 0.174, 0.231, 0.080, 0.000,…
$ eccentricity_error_min <dbl> 0.008, 0.254, 0.154, 0.005, 0.030, NA, 0.…
$ eccentricity_error_max <dbl> 0.009, 0.110, 0.110, 0.005, 0.030, NA, 0.…
$ inclination <dbl> 86.116, NA, 47.738, NA, NA, NA, 35.700, 8…
$ inclination_error_min <dbl> 20.530, NA, 11.804, NA, NA, NA, 3.200, 14…
$ inclination_error_max <dbl> 19.957, NA, 12.651, NA, NA, NA, 3.200, 14…
$ angular_distance <dbl> 0.066339, NA, NA, 0.011664, 0.012887, 0.0…
$ discovered <dbl> 2000, 2022, 2022, 2007, 2009, 2008, 2002,…
$ updated <date> 2024-06-14, 2024-06-14, 2024-06-14, 2024…
$ omega <dbl> 112.816, 279.492, 79.772, 94.800, 117.630…
$ omega_error_min <dbl> 5.448, 67.524, 31.067, 1.500, 21.060, NA,…
$ omega_error_max <dbl> 5.254, 30.206, 15.493, 1.500, 21.060, NA,…
$ tperi <dbl> 2451492, NA, NA, 2452900, 2452861, 245286…
$ tperi_error_min <dbl> 17.00, NA, NA, 1.60, 2.06, 1.50, 5.00, 33…
$ tperi_error_max <dbl> 17.00, NA, NA, 1.60, 2.06, 1.50, 5.00, 33…
$ tconj <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tconj_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tconj_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_tr <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_tr_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_tr_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_tr_sec <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_tr_sec_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_tr_sec_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ lambda_angle <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ lambda_angle_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ lambda_angle_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ impact_parameter <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ impact_parameter_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ impact_parameter_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_vr <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_vr_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_vr_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ k <dbl> 114.583, 4.157, 42.503, 302.800, 189.700,…
$ k_error_min <dbl> 1.196, NA, 0.586, 2.600, 7.150, 1.300, 0.…
$ k_error_max <dbl> 1.067, NA, 1.868, 2.600, 7.150, 1.300, 0.…
$ temp_calculated <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ temp_calculated_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ temp_calculated_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ temp_measured <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1…
$ hot_point_lon <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ geometric_albedo <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ geometric_albedo_error_min <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ geometric_albedo_error_max <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ log_g <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4…
$ publication <chr> "Published in a refereed paper", "Publish…
$ detection_type <chr> "Radial Velocity, Astrometry", "Radial Ve…
$ mass_measurement_type <chr> "Radial Velocity", "Radial Velocity", "As…
$ radius_measurement_type <chr> "Theoretical", NA, NA, NA, NA, NA, NA, NA…
$ alternate_names <chr> "HD 10697 b", "HD 12235 b", "HD 12235 c",…
$ molecules <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "…
$ star_name <chr> "109 Psc", "112 Psc", "112 Psc", "11 Com …
$ ra <dbl> 26.23260, 30.03816, 30.03816, 185.17917, …
$ dec <dbl> 20.0831500, 3.0970140, 3.0970140, 17.7927…
$ mag_v <dbl> 6.290, 5.880, 5.880, 4.740, 5.020, 5.220,…
$ mag_i <dbl> NA, NA, NA, NA, NA, 4.100, NA, NA, NA, NA…
$ mag_j <dbl> NA, 5.204, 5.204, NA, NA, 3.020, NA, NA, …
$ mag_h <dbl> NA, 4.630, 4.630, NA, NA, 2.610, NA, NA, …
$ mag_k <dbl> NA, 4.494, 4.494, NA, NA, 2.330, NA, NA, …
$ star_distance <dbl> 32.5600, 31.7627, 31.7627, 110.6000, 119.…
$ star_distance_error_min <dbl> 0.88000, 0.10695, 0.10695, 10.50000, 6.90…
$ star_distance_error_max <dbl> 0.88000, 0.10695, 0.10695, 10.50000, 6.90…
$ star_metallicity <dbl> 0.100, 0.310, 0.310, -0.350, 0.040, -0.24…
$ star_metallicity_error_min <dbl> 0.060, 0.100, 0.100, 0.090, 0.040, NA, 0.…
$ star_metallicity_error_max <dbl> 0.060, 0.100, 0.100, 0.090, 0.040, NA, 0.…
$ star_mass <dbl> 1.13, 1.10, 1.10, 2.70, 1.80, 2.20, 0.90,…
$ star_mass_error_min <dbl> 0.030, 0.133, 0.133, 0.300, 0.250, 0.200,…
$ star_mass_error_max <dbl> 0.030, 0.133, 0.133, 0.300, 0.250, 0.200,…
$ star_radius <dbl> 1.790, 1.801, 1.801, 19.000, 24.080, 11.0…
$ star_radius_error_min <dbl> 0.1700, 0.0725, 0.0725, 2.0000, 1.8400, 1…
$ star_radius_error_max <dbl> 0.1700, 0.0725, 0.0725, 2.0000, 1.8400, 1…
$ star_sp_type <chr> "G5 IV", "G0IV", "G0IV", "G8III", "K4III"…
$ star_age <dbl> 6.900, NA, NA, NA, 1.560, NA, 5.100, 5.10…
$ star_age_error_min <dbl> 0.600, NA, NA, NA, 0.540, NA, NA, NA, 1.8…
$ star_age_error_max <dbl> 0.600, NA, NA, NA, 0.540, NA, NA, NA, 1.8…
$ star_teff <dbl> 5600.00, 5986.00, 5986.00, 4742.00, 4340.…
$ star_teff_error_min <dbl> 80.000, 105.437, 105.437, 100.000, 70.000…
$ star_teff_error_max <dbl> 80.000, 105.437, 105.437, 100.000, 70.000…
$ star_detected_disc <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ star_magnetic_field <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ star_alternate_names <chr> "HD 10697", "HD 12235", "HD 12235", "HD 1…library(naniar)
gg_miss_var(exoplanets)
library(visdat)
vis_dat(exoplanets)
names(exoplanets) [1] "name" "planet_status"
[3] "mass" "mass_error_min"
[5] "mass_error_max" "mass_sini"
[7] "mass_sini_error_min" "mass_sini_error_max"
[9] "radius" "radius_error_min"
[11] "radius_error_max" "orbital_period"
[13] "orbital_period_error_min" "orbital_period_error_max"
[15] "semi_major_axis" "semi_major_axis_error_min"
[17] "semi_major_axis_error_max" "eccentricity"
[19] "eccentricity_error_min" "eccentricity_error_max"
[21] "inclination" "inclination_error_min"
[23] "inclination_error_max" "angular_distance"
[25] "discovered" "updated"
[27] "omega" "omega_error_min"
[29] "omega_error_max" "tperi"
[31] "tperi_error_min" "tperi_error_max"
[33] "tconj" "tconj_error_min"
[35] "tconj_error_max" "tzero_tr"
[37] "tzero_tr_error_min" "tzero_tr_error_max"
[39] "tzero_tr_sec" "tzero_tr_sec_error_min"
[41] "tzero_tr_sec_error_max" "lambda_angle"
[43] "lambda_angle_error_min" "lambda_angle_error_max"
[45] "impact_parameter" "impact_parameter_error_min"
[47] "impact_parameter_error_max" "tzero_vr"
[49] "tzero_vr_error_min" "tzero_vr_error_max"
[51] "k" "k_error_min"
[53] "k_error_max" "temp_calculated"
[55] "temp_calculated_error_min" "temp_calculated_error_max"
[57] "temp_measured" "hot_point_lon"
[59] "geometric_albedo" "geometric_albedo_error_min"
[61] "geometric_albedo_error_max" "log_g"
[63] "publication" "detection_type"
[65] "mass_measurement_type" "radius_measurement_type"
[67] "alternate_names" "molecules"
[69] "star_name" "ra"
[71] "dec" "mag_v"
[73] "mag_i" "mag_j"
[75] "mag_h" "mag_k"
[77] "star_distance" "star_distance_error_min"
[79] "star_distance_error_max" "star_metallicity"
[81] "star_metallicity_error_min" "star_metallicity_error_max"
[83] "star_mass" "star_mass_error_min"
[85] "star_mass_error_max" "star_radius"
[87] "star_radius_error_min" "star_radius_error_max"
[89] "star_sp_type" "star_age"
[91] "star_age_error_min" "star_age_error_max"
[93] "star_teff" "star_teff_error_min"
[95] "star_teff_error_max" "star_detected_disc"
[97] "star_magnetic_field" "star_alternate_names" library(janitor)
exoplanets %>% tabyl(planet_status) planet_status n percent
Confirmed 7418 1library(data.table)data.table 1.17.0 using 4 threads (see ?getDTthreads). Latest news: r-datatable.com# options(repr.matrix.max.rows=100)
exoplanets %>%
add_prop_miss() %>%
arrange(prop_miss_all) %>%
head(5) %>%
data.table::transpose(keep.names="column") -> preview
preview# preview %>% View()We have a lot of features: - Planet name - Mass (M jup) - Mass*sin(i) (M jup) - This describes minimum mass of the planet due to inclination effect - Radius (Rjup) - Period (day) - a / the average distance of the planet and its star - it’s in AU (astronomical units), which is the standard distance used for these types of things - 1 AU is the average distance tween the earth and the sun - e / eccentry of a planet (between 0 and 1) - represenets how much of a circle is the orbit - e = 0 means perfect circle, e > 1 means its not bound to the star - Discovery - year when it was discovered - update - year it was updated -
conflicts_prefer(dplyr::filter)[conflicted] Will prefer dplyr::filter over any other package.exoplanets %>%
filter(name %>% str_like("%TOI-784%"))conflicts_prefer(dplyr::filter)[conflicted] Removing existing preference.[conflicted] Will prefer dplyr::filter over any other package.exoplanets %>%
filter(discovered == 2023)exoplanets %>%
mutate(
ra_rad = ra, # Convert RA to radians
dec_rad = dec # Convert Dec to radians
) %>%
ggplot(aes(x = ra_rad, y = dec_rad, color = dec)) +
geom_point(size = 0.4) +
coord_map("aitoff") + # Apply Aitoff projection
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none" # Optionally remove legend
)
# check columsn that start with star
exoplanets %>%
select(starts_with("star"))library(dplyr)
library(plotly)
conflicts_prefer(plotly::layout)[conflicted] Will prefer plotly::layout over any other package.# Create a new column to distinguish Kepler exoplanets
exoplanets_3d <- exoplanets %>%
mutate(
ra_rad = ra * pi / 180, # Convert RA from degrees to radians
dec_rad = dec * pi / 180, # Convert Dec from degrees to radians
x = cos(dec_rad) * cos(ra_rad), # Convert to Cartesian coordinates
y = cos(dec_rad) * sin(ra_rad),
z = sin(dec_rad),
color = case_when( # Create a column for red when kepler, blue otherwise
str_detect(paste(name, alternate_names), regex("kepler|koi", ignore_case = TRUE)) ~ "Kepler",
# if it's free floating (star_name is NA)
star_name %>% is.na() ~ "Free Floating",
TRUE ~ "Other"
),
hover_text = paste("Name: ", name) # Create custom hover text with the name of the exoplanet
)
# Define steps for opacity slider
steps <- list(
list(args = list("marker.opacity", 0.0), label = "0.0", method = "restyle"),
list(args = list("marker.opacity", 0.1), label = "0.1", method = "restyle"),
list(args = list("marker.opacity", 0.2), label = "0.2", method = "restyle"),
list(args = list("marker.opacity", 0.3), label = "0.3", method = "restyle"),
list(args = list("marker.opacity", 0.4), label = "0.4", method = "restyle"),
list(args = list("marker.opacity", 0.5), label = "0.5", method = "restyle"),
list(args = list("marker.opacity", 0.6), label = "0.6", method = "restyle"),
list(args = list("marker.opacity", 0.7), label = "0.7", method = "restyle"),
list(args = list("marker.opacity", 0.8), label = "0.8", method = "restyle"),
list(args = list("marker.opacity", 0.9), label = "0.9", method = "restyle"),
list(args = list("marker.opacity", 1.0), label = "1.0", method = "restyle")
)
# Create an interactive 3D scatter plot with plotly
plot_ly(
data = exoplanets_3d,
x = ~x,
y = ~y,
z = ~z,
color = ~color, # Use the kepler_highlight column for color mapping
colors = c("Other" = "red", "Kepler" = "blue", "Free Floating" = "green"),
text = ~hover_text, # Show the name of the exoplanet on hover
type = "scatter3d",
mode = "markers",
marker = list(size = 1, opacity = 0.7), # Default opacity
showlegend = TRUE
) %>%
layout(
title = "3D Sky Map of Exoplanets (Kepler Highlighted)",
scene = list(
xaxis = list(title = "X"),
yaxis = list(title = "Y"),
zaxis = list(title = "Z")
),
sliders = list(
list(
active = 1, # Set the default opacity value to 1.0 (fully opaque)
currentvalue = list(
prefix = "Opacity: ",
font = list(size = 15)
),
pad = list(t = 60),
steps = steps # Use the steps defined earlier for the opacity slider
)
)
)Warning: Ignoring 1 observationsWarning: Ignoring 1 observations
# Assuming your data is loaded as 'exoplanets'
# Convert RA to degrees (if it's in hours:minutes:seconds format)
# If RA is already in degrees, skip this step
exoplanets %>%
mutate(
ra_deg = ra, # Convert RA from hours to degrees (if needed)
# Convert to polar coordinates for plotting
# RA is mapped to theta (0-360 degrees)
theta = ra_deg
) %>%
ggplot(aes(x = theta, y = star_distance, color = mass)) +
# Use coord_polar for circular plot
coord_polar(start = 0, direction = -1) + # Start at 0 degrees, clockwise direction
# Add concentric circles for distance reference
geom_hline(yintercept = c(10, 100, 1000, 10000),
color = "gray", linetype = "solid", size = 0.3, alpha = 0.7) +
# Add radial lines for angle reference
geom_vline(xintercept = seq(0, 330, by = 30),
color = "gray", linetype = "solid", size = 0.3, alpha = 0.7) +
# Plot the exoplanets
geom_point(alpha = 0.8, size = 1) +
# Use log scale for distance
scale_y_log10(
breaks = c(10, 100, 1000, 10000),
labels = c("10 pc", "100 pc", "1000 pc", "10000 pc"),
limits = c(1, 15000)
) +
# Use log scale for mass colors
scale_color_gradientn(
colors = c("#1E90FF", "#32CD32", "#FFFF00", "#FFA500", "#FF4500", "#FF0000"),
trans = "log10",
breaks = c(0.0001, 0.001, 0.01, 0.1, 1, 10),
labels = c("10⁻⁴", "10⁻³", "10⁻²", "10⁻¹", "10⁰", "10¹"),
name = "Planetary Mass (MJup)"
) +
# Remove grid and axis elements
theme_minimal() +
theme(
axis.title = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_blank(),
panel.grid = element_blank(),
legend.position = "bottom",
legend.box = "horizontal",
plot.title = element_text(hjust = 0.5)
) +
ggtitle("Exoplanet Distribution")Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
Please use `linewidth` instead.
library(dplyr)
library(plotly)
# Create a new column to distinguish Kepler exoplanets
exoplanets_3d <- exoplanets %>%
mutate(
ra_rad = ra * pi / 180, # Convert RA from degrees to radians
dec_rad = dec * pi / 180, # Convert Dec from degrees to radians
x = cos(dec_rad) * cos(ra_rad), # Convert to Cartesian coordinates
y = cos(dec_rad) * sin(ra_rad),
z = sin(dec_rad),
color = case_when( # Create a column for red when kepler, blue otherwise
str_detect(paste(name, alternate_names), regex("kepler|koi", ignore_case = TRUE)) ~ "Kepler",
# if it's free floating (star_name is NA)
star_name %>% is.na() ~ "Free Floating",
TRUE ~ "Other"
),
hover_text = paste("Name: ", name), # Create custom hover text with the name of the exoplanet
scaled_x = x * (1 / star_distance), # Adjust x coordinate by star distance (closer = closer to center)
scaled_y = y * (1 / star_distance), # Adjust y coordinate similarly
scaled_z = z * (1 / star_distance) # Adjust z coordinate similarly
)
# Define steps for opacity slider
steps <- list(
list(args = list("marker.opacity", 0.0), label = "0.0", method = "restyle"),
list(args = list("marker.opacity", 0.1), label = "0.1", method = "restyle"),
list(args = list("marker.opacity", 0.2), label = "0.2", method = "restyle"),
list(args = list("marker.opacity", 0.3), label = "0.3", method = "restyle"),
list(args = list("marker.opacity", 0.4), label = "0.4", method = "restyle"),
list(args = list("marker.opacity", 0.5), label = "0.5", method = "restyle"),
list(args = list("marker.opacity", 0.6), label = "0.6", method = "restyle"),
list(args = list("marker.opacity", 0.7), label = "0.7", method = "restyle"),
list(args = list("marker.opacity", 0.8), label = "0.8", method = "restyle"),
list(args = list("marker.opacity", 0.9), label = "0.9", method = "restyle"),
list(args = list("marker.opacity", 1.0), label = "1.0", method = "restyle")
)
# Create an interactive 3D scatter plot with plotly
fig <- plot_ly(
data = exoplanets_3d,
x = ~scaled_x,
y = ~scaled_y,
z = ~scaled_z,
color = ~color, # Use the kepler_highlight column for color mapping
colors = c("Other" = "red", "Kepler" = "blue", "Free Floating" = "green"),
text = ~hover_text, # Show the name of the exoplanet on hover
type = "scatter3d",
mode = "markers",
marker = list(size = 2, opacity = 0.7), # Default opacity
showlegend = TRUE
)
# Add layout with a slider for opacity
fig <- fig %>% layout(
title = "3D Sky Map of Exoplanets (Kepler Highlighted)",
scene = list(
xaxis = list(title = "X"),
yaxis = list(title = "Y"),
zaxis = list(title = "Z")
),
sliders = list(
list(
active = 1, # Set the default opacity value to 1.0 (fully opaque)
currentvalue = list(
prefix = "Opacity: ",
font = list(size = 15)
),
pad = list(t = 60),
steps = steps # Use the steps defined earlier for the opacity slider
)
)
)
figWarning: Ignoring 357 observationsWarning: Ignoring 357 observationsexoplanets %>% names() [1] "name" "planet_status"
[3] "mass" "mass_error_min"
[5] "mass_error_max" "mass_sini"
[7] "mass_sini_error_min" "mass_sini_error_max"
[9] "radius" "radius_error_min"
[11] "radius_error_max" "orbital_period"
[13] "orbital_period_error_min" "orbital_period_error_max"
[15] "semi_major_axis" "semi_major_axis_error_min"
[17] "semi_major_axis_error_max" "eccentricity"
[19] "eccentricity_error_min" "eccentricity_error_max"
[21] "inclination" "inclination_error_min"
[23] "inclination_error_max" "angular_distance"
[25] "discovered" "updated"
[27] "omega" "omega_error_min"
[29] "omega_error_max" "tperi"
[31] "tperi_error_min" "tperi_error_max"
[33] "tconj" "tconj_error_min"
[35] "tconj_error_max" "tzero_tr"
[37] "tzero_tr_error_min" "tzero_tr_error_max"
[39] "tzero_tr_sec" "tzero_tr_sec_error_min"
[41] "tzero_tr_sec_error_max" "lambda_angle"
[43] "lambda_angle_error_min" "lambda_angle_error_max"
[45] "impact_parameter" "impact_parameter_error_min"
[47] "impact_parameter_error_max" "tzero_vr"
[49] "tzero_vr_error_min" "tzero_vr_error_max"
[51] "k" "k_error_min"
[53] "k_error_max" "temp_calculated"
[55] "temp_calculated_error_min" "temp_calculated_error_max"
[57] "temp_measured" "hot_point_lon"
[59] "geometric_albedo" "geometric_albedo_error_min"
[61] "geometric_albedo_error_max" "log_g"
[63] "publication" "detection_type"
[65] "mass_measurement_type" "radius_measurement_type"
[67] "alternate_names" "molecules"
[69] "star_name" "ra"
[71] "dec" "mag_v"
[73] "mag_i" "mag_j"
[75] "mag_h" "mag_k"
[77] "star_distance" "star_distance_error_min"
[79] "star_distance_error_max" "star_metallicity"
[81] "star_metallicity_error_min" "star_metallicity_error_max"
[83] "star_mass" "star_mass_error_min"
[85] "star_mass_error_max" "star_radius"
[87] "star_radius_error_min" "star_radius_error_max"
[89] "star_sp_type" "star_age"
[91] "star_age_error_min" "star_age_error_max"
[93] "star_teff" "star_teff_error_min"
[95] "star_teff_error_max" "star_detected_disc"
[97] "star_magnetic_field" "star_alternate_names" # check how many are missing
exoplanets %>%
select(ra, dec, angular_distance) %>%
mutate(ra = ra %>% is.na(), dec = dec %>% is.na(), angular_distance = angular_distance %>% is.na()) %>%
summarise_all(mean) %>%
gather(key="column", value="percentage")# check which ones dont have ra
exoplanets %>%
filter(ra %>% is.na())# check out alternate names
exoplanets %>%
select(name, alternate_names) %>%
filter(alternate_names %>% str_length() > 0)NAexoplanets %>%
tabyl(publication) publication n percent
Announced on a professional conference 55 0.007414397
Announced on a website 2357 0.317740631
Published in a refereed paper 4873 0.656915611
Submitted to a professional journal 133 0.017929361# remove any column with error in the name
exoplanets_r <- exoplanets %>%
select(-contains("error")) %>%
select(-planet_status, -publication) %>% # useless
select(-hot_point_lon, ) # too many missings
exoplanets_r %>% names [1] "name" "mass"
[3] "mass_sini" "radius"
[5] "orbital_period" "semi_major_axis"
[7] "eccentricity" "inclination"
[9] "angular_distance" "discovered"
[11] "updated" "omega"
[13] "tperi" "tconj"
[15] "tzero_tr" "tzero_tr_sec"
[17] "lambda_angle" "impact_parameter"
[19] "tzero_vr" "k"
[21] "temp_calculated" "temp_measured"
[23] "geometric_albedo" "log_g"
[25] "detection_type" "mass_measurement_type"
[27] "radius_measurement_type" "alternate_names"
[29] "molecules" "star_name"
[31] "ra" "dec"
[33] "mag_v" "mag_i"
[35] "mag_j" "mag_h"
[37] "mag_k" "star_distance"
[39] "star_metallicity" "star_mass"
[41] "star_radius" "star_sp_type"
[43] "star_age" "star_teff"
[45] "star_detected_disc" "star_magnetic_field"
[47] "star_alternate_names" library(visdat)
vis_dat(exoplanets_r)
vis_miss(exoplanets_r, sort_miss = T, cluster = T)
detection type
exoplanets %>%
tabyl("detection_type") %>%
arrange(-n) detection_type n percent
Primary Transit 4509 0.6078457805
Radial Velocity 1145 0.1543542734
Imaging 922 0.1242922621
Microlensing 313 0.0421946616
Timing 160 0.0215691561
Radial Velocity, Astrometry 99 0.0133459153
Imaging, Astrometry 49 0.0066055541
Astrometry 46 0.0062011324
Imaging, Other 46 0.0062011324
Other 42 0.0056619035
TTV 32 0.0043138312
Timing, Kinematic 10 0.0013480723
Primary Transit, Radial Velocity 7 0.0009436506
Radial Velocity, Primary Transit 7 0.0009436506
Timing, Other 6 0.0008088434
Astrometry, Radial Velocity 3 0.0004044217
Imaging, Other, Kinematic 3 0.0004044217
Imaging, Kinematic 2 0.0002696145
Kinematic 2 0.0002696145
Primary Transit, TTV 2 0.0002696145
Radial Velocity, Imaging 2 0.0002696145
Astrometry, Imaging 1 0.0001348072
Imaging, Other, Astrometry 1 0.0001348072
Imaging, Primary Transit 1 0.0001348072
Imaging, Radial Velocity, Astrometry 1 0.0001348072
Other, Imaging 1 0.0001348072
Other, Imaging, Kinematic 1 0.0001348072
Other, Radial Velocity 1 0.0001348072
Primary Transit, Astrometry 1 0.0001348072
Primary Transit, Kinematic 1 0.0001348072
Radial Velocity, Timing 1 0.0001348072
Timing, Astrometry 1 0.0001348072library(fastDummies)
exoplanets_rd <- exoplanets_r %>%
dummy_cols(select_columns = "detection_type", split = ", ")
exoplanets_rd %>% select(starts_with("detection_type")) %>%
uniqueexoplanets_rd %>%
select(starts_with("detection_type")) %>%
gather(key="detection_type", value="value") %>%
filter(value == 1) %>%
group_by(detection_type) %>%
summarise(n = n(), percentage = n()*100 / nrow(exoplanets_rd)) %>%
arrange(-n)library(naniar)
exoplanets_rd %>%
group_by(`detection_type_Primary Transit`) %>%
miss_var_summary() %>%
arrange(variable) %>%
filter(variable %>% str_detect("detection_type", negate = T)) %>%
ggplot(aes(x = variable, y = pct_miss, fill = `detection_type_Primary Transit`)) +
geom_col(position="dodge") +
coord_flip() 
if (F){
library(misty)
exoplanets_rd %>%
select(tzero_vr, tzero_tr_sec, tzero_tr) %>%
na.test(data = exoplanets_rd)
} # didnt work for some reasonlibrary(shiny)
library(dplyr)
library(plotly)
library(naniar) # Assuming miss_var_summary() is from naniar
# Sample UI
ui <- fluidPage(
titlePanel("Missing Data by Detection Type"),
sidebarLayout(
sidebarPanel(
selectInput("group_var", "Select Detection Type:",
choices = names(exoplanets_rd)[grepl("^detection_type_", names(exoplanets_rd))])
),
mainPanel(
plotlyOutput("missing_plot", height = "700px") # Increased height
)
)
)
# Server function
server <- function(input, output) {
output$missing_plot <- renderPlotly({
exoplanets_rd %>%
group_by(.data[[input$group_var]]) %>%
miss_var_summary() %>%
arrange(variable) %>%
filter(!str_detect(variable, "detection_type")) %>%
plot_ly(y = ~variable, x = ~pct_miss, color = ~.data[[input$group_var]], type = "bar") %>%
layout(barmode = "group", height = 700) # Increased plot height
})
}
# Run the app
if (F) {
shinyApp(ui = ui, server = server)
}Kepler
# filter by the kepler
exoplanets %>%
filter(paste(name, alternate_names) %>% str_like("%Kepler%")) %>%
tabyl("detection_type") detection_type n percent
Other 6 0.0021543986
Primary Transit 2722 0.9773788151
Primary Transit, TTV 2 0.0007181329
Radial Velocity 26 0.0093357271
Radial Velocity, Primary Transit 1 0.0003590664
TTV 23 0.0082585278
Timing 5 0.0017953321# check other
exoplanets %>%
filter(detection_type == "Other")exoplanets_rd %>%
select(name, star_distance, star_name)conflicts_prefer(lubridate::yday)[conflicted] Will prefer lubridate::yday over any other package.conflicts_prefer(lubridate::year)[conflicted] Will prefer lubridate::year over any other package.year_with_percentage <- function(date) {
percentage_of_year <- yday(date) / ifelse(leap_year(date), 366, 365)
year(date) + percentage_of_year
}
exoplanets_rd %>%
mutate(updated = updated %>% year_with_percentage) %>%
mutate(diff_disc_updated = updated - discovered) -> exoplanets_rdd
exoplanets_rdd %>%
select(discovered, updated, diff_disc_updated)exoplanets_rddk <- exoplanets_rdd %>%
mutate(is_kepler = paste(name, alternate_names) %>% str_detect("kepler" %>% regex(ignore_case = T)))
exoplanets_rddk %>%
select(name, is_kepler) %>%
arrange(-is_kepler)exoplanets %>%
filter(publication %>% is_na)Modeling
# transform into is shadow matrix
library(naniar)
exoplanets_rddk %>%
select(-name, -discovered, -updated, -diff_disc_updated, -is_kepler, -star_distance, -starts_with("detection_type")) %>%
janitor::remove_constant() %>%
as_shadow() -> shadow_matrix
# add columns to exoplanets_rd
shadow_exoplanets <- exoplanets_rddk %>%
bind_cols(shadow_matrix) %>%
# select everyone that ends with _NA
select(name, starts_with("detection_type_"), discovered, updated, diff_disc_updated, is_kepler, star_distance, ra, dec, ends_with("_NA")) %>%
# change detection_type to factor
mutate_at(vars(starts_with("detection_type_")), as.factor) %>%
janitor::clean_names()
# TODO reduce dimensionality on the _NA
shadow_exoplanetsclustering
TODO
model
shadow_exoplanets %>% glimpseRows: 7,418
Columns: 58
$ name <chr> "109 Psc b", "112 Psc b", "112 Psc c"…
$ detection_type_astrometry <fct> 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1…
$ detection_type_imaging <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1…
$ detection_type_radial_velocity <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0…
$ detection_type_kinematic <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ detection_type_other <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ detection_type_primary_transit <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ detection_type_microlensing <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ detection_type_ttv <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ detection_type_timing <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ discovered <dbl> 2000, 2022, 2022, 2007, 2009, 2008, 2…
$ updated <dbl> 2024.454, 2024.454, 2024.454, 2024.58…
$ diff_disc_updated <dbl> 24.4535519, 2.4535519, 2.4535519, 17.…
$ is_kepler <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FA…
$ star_distance <dbl> 32.5600, 31.7627, 31.7627, 110.6000, …
$ ra <dbl> 26.23260, 30.03816, 30.03816, 185.179…
$ dec <dbl> 20.0831500, 3.0970140, 3.0970140, 17.…
$ mass_na <fct> !NA, NA, !NA, NA, NA, NA, !NA, !NA, N…
$ mass_sini_na <fct> !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA…
$ radius_na <fct> !NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ orbital_period_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ semi_major_axis_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ eccentricity_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ inclination_na <fct> !NA, NA, !NA, NA, NA, NA, !NA, !NA, N…
$ angular_distance_na <fct> !NA, NA, NA, !NA, !NA, !NA, !NA, !NA,…
$ omega_na <fct> !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA…
$ tperi_na <fct> !NA, NA, NA, !NA, !NA, !NA, !NA, !NA,…
$ tconj_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_tr_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_tr_sec_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ lambda_angle_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ impact_parameter_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_vr_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ k_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ temp_calculated_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ temp_measured_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ geometric_albedo_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ log_g_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ mass_measurement_type_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ radius_measurement_type_na <fct> !NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ alternate_names_na <fct> !NA, !NA, !NA, NA, NA, NA, NA, !NA, N…
$ molecules_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ star_name_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ ra_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ dec_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ mag_v_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ mag_i_na <fct> NA, NA, NA, NA, NA, !NA, NA, NA, NA, …
$ mag_j_na <fct> NA, !NA, !NA, NA, NA, !NA, NA, NA, NA…
$ mag_h_na <fct> NA, !NA, !NA, NA, NA, !NA, NA, NA, NA…
$ mag_k_na <fct> NA, !NA, !NA, NA, NA, !NA, NA, NA, NA…
$ star_metallicity_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ star_mass_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ star_radius_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ star_sp_type_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ star_age_na <fct> !NA, NA, NA, NA, !NA, NA, !NA, !NA, !…
$ star_teff_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ star_detected_disc_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ star_alternate_names_na <fct> !NA, !NA, !NA, !NA, NA, NA, NA, NA, !…library(rpart)
library(dplyr)
library(purrr)
set.seed(123)
# Define target and predictor columns
target_cols <- names(shadow_exoplanets) %>%
keep(~ startsWith(.x, "detection_type_"))
predictor_cols <- names(shadow_exoplanets) %>%
setdiff(c("name", target_cols))
# Train decision trees for each target label
models <- target_cols %>%
set_names() %>%
map(~ rpart(as.formula(paste(.x, "~", paste(predictor_cols, collapse = " + "))),
data = shadow_exoplanets, method = "class"))
# Make predictions and add them to the original dataset
shadow_exoplanets_with_preds <- shadow_exoplanets %>%
bind_cols(models %>%
map_dfc(~ predict(.x, shadow_exoplanets, type = "class")) %>%
rename_with(~ paste0("pred_", target_cols)) # Prefix predictions for clarity
)
predictions <- shadow_exoplanets_with_preds %>%
mutate(
actual_combined = apply(select(., all_of(target_cols)), 1, paste, collapse = "_"),
predicted_combined = apply(select(., starts_with("pred_")), 1, paste, collapse = "_")
) %>% select(actual_combined, predicted_combined, starts_with("pred_"), starts_with("detection_type_"))
predictionsmulti_label_confusion_matrix <- function(y_true, y_pred) {
result <- list()
for (col in names(y_true)) {
confusion_matrix <- table(y_true[[col]], y_pred[[paste0("pred_", col)]])
result[[col]] <- confusion_matrix
}
return(result)
}
multi_label_confusion_matrix(shadow_exoplanets_with_preds %>% select(starts_with("detection_type_")), predictions %>% select(starts_with("pred_")))$detection_type_astrometry
0 1
0 7194 22
1 86 116
$detection_type_imaging
0 1
0 6335 53
1 58 972
$detection_type_radial_velocity
0 1
0 6075 77
1 83 1183
$detection_type_kinematic
0 1
0 7399 0
1 19 0
$detection_type_other
0 1
0 7314 3
1 70 31
$detection_type_primary_transit
0 1
0 2785 105
1 70 4458
$detection_type_microlensing
0 1
0 7096 9
1 14 299
$detection_type_ttv
0 1
0 7383 1
1 26 8
$detection_type_timing
0 1
0 7222 18
1 48 130# Load necessary library
library(rpart.plot)
# Plot the decision trees with titles
target_cols %>%
map2(models, ~ {
rpart.plot(.y,
type = 4,
extra = 101,
under = TRUE,
fallen.leaves = TRUE,
main = paste("Decision Tree for", .x)) # Title with the target label
})
[[1]]
[[1]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 202 0 (0.972768940 0.027231060)
2) star_distance>=108.984 5157 26 0 (0.994958309 0.005041691) *
3) star_distance< 108.984 2261 176 0 (0.922158337 0.077841663)
6) mass_na=NA 968 1 0 (0.998966942 0.001033058) *
7) mass_na=!NA 1293 175 0 (0.864655839 0.135344161)
14) radius_na=!NA 690 18 0 (0.973913043 0.026086957) *
15) radius_na=NA 603 157 0 (0.739635158 0.260364842)
30) inclination_na=NA 371 42 0 (0.886792453 0.113207547)
60) updated< 2024.638 260 12 0 (0.953846154 0.046153846) *
61) updated>=2024.638 111 30 0 (0.729729730 0.270270270)
122) mass_measurement_type_na=NA 59 2 0 (0.966101695 0.033898305) *
123) mass_measurement_type_na=!NA 52 24 1 (0.461538462 0.538461538)
246) updated>=2024.642 45 21 0 (0.533333333 0.466666667)
492) dec>=-36.07075 34 12 0 (0.647058824 0.352941176) *
493) dec< -36.07075 11 2 1 (0.181818182 0.818181818) *
247) updated< 2024.642 7 0 1 (0.000000000 1.000000000) *
31) inclination_na=!NA 232 115 0 (0.504310345 0.495689655)
62) updated< 2024.422 53 4 0 (0.924528302 0.075471698) *
63) updated>=2024.422 179 68 1 (0.379888268 0.620111732)
126) diff_disc_updated>=4.168493 90 37 0 (0.588888889 0.411111111)
252) dec>=-47.80806 80 28 0 (0.650000000 0.350000000)
504) omega_na=NA 12 0 0 (1.000000000 0.000000000) *
505) omega_na=!NA 68 28 0 (0.588235294 0.411764706)
1010) ra< 242.6082 51 16 0 (0.686274510 0.313725490)
2020) star_distance< 41.1811 36 7 0 (0.805555556 0.194444444) *
2021) star_distance>=41.1811 15 6 1 (0.400000000 0.600000000) *
1011) ra>=242.6082 17 5 1 (0.294117647 0.705882353) *
253) dec< -47.80806 10 1 1 (0.100000000 0.900000000) *
127) diff_disc_updated< 4.168493 89 15 1 (0.168539326 0.831460674)
254) tperi_na=!NA 11 4 0 (0.636363636 0.363636364) *
255) tperi_na=NA 78 8 1 (0.102564103 0.897435897) *
[[1]]$snipped.nodes
NULL
[[1]]$xlim
[1] 0 1
[[1]]$ylim
[1] 0 1
[[1]]$x
[1] 0.11257349 0.02115644 0.20399053 0.08593918 0.32204188 0.15072191 0.49336185 0.28433631 0.21550465 0.35316796 0.28028739 0.42604854 0.37746149 0.34507012
[15] 0.40985286 0.47463559 0.70238740 0.53941833 0.86535647 0.77020683 0.67708164 0.60420106 0.74996222 0.70137517 0.66898380 0.73376654 0.79854927 0.86333201
[29] 0.96050611 0.92811474 0.99289748
[[1]]$y
[1] 0.97711079 0.01400365 0.88955560 0.01400365 0.80200040 0.01400365 0.71444521 0.62689001 0.01400365 0.53933482 0.01400365 0.45177962 0.36422443 0.01400365
[15] 0.01400365 0.01400365 0.62689001 0.01400365 0.53933482 0.45177962 0.36422443 0.01400365 0.27666923 0.18911404 0.01400365 0.01400365 0.01400365 0.01400365
[29] 0.45177962 0.01400365 0.01400365
[[1]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
x 0.1125735 0.02115644 0.2039905 0.08593918 0.3220419 0.1507219 0.4933619 0.2843363 0.2155046 0.3531680 0.2802874 0.4260485 0.3774615 0.3450701 0.4098529
NA 0.02115644 0.2039905 0.08593918 0.3220419 0.1507219 0.4933619 0.2843363 0.2155046 0.3531680 0.2802874 0.4260485 0.3774615 0.3450701 0.4098529
NA 0.11257349 0.1125735 0.20399053 0.2039905 0.3220419 0.3220419 0.4933619 0.2843363 0.2843363 0.3531680 0.3531680 0.4260485 0.3774615 0.3774615
[,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31]
x 0.4746356 0.7023874 0.5394183 0.8653565 0.7702068 0.6770816 0.6042011 0.7499622 0.7013752 0.6689838 0.7337665 0.7985493 0.8633320 0.9605061 0.9281147 0.9928975
0.4746356 0.7023874 0.5394183 0.8653565 0.7702068 0.6770816 0.6042011 0.7499622 0.7013752 0.6689838 0.7337665 0.7985493 0.8633320 0.9605061 0.9281147 0.9928975
0.4260485 0.4933619 0.7023874 0.7023874 0.8653565 0.7702068 0.6770816 0.6770816 0.7499622 0.7013752 0.7013752 0.7499622 0.7702068 0.8653565 0.9605061 0.9605061
[[1]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
y 0.9990316 0.03592447 0.9114764 0.03592447 0.8239212 0.03592447 0.7363660 0.6488108 0.03592447 0.5612556 0.03592447 0.4737004 0.3861452 0.03592447 0.03592447
NA 0.99165086 0.9916509 0.90409567 0.9040957 0.81654047 0.8165405 0.7289853 0.64143008 0.6414301 0.55387489 0.5538749 0.4663197 0.37876450 0.37876450
NA 0.99165086 0.9916509 0.90409567 0.9040957 0.81654047 0.8165405 0.7289853 0.64143008 0.6414301 0.55387489 0.5538749 0.4663197 0.37876450 0.37876450
[,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30]
y 0.03592447 0.6488108 0.03592447 0.5612556 0.4737004 0.3861452 0.03592447 0.2985901 0.2110349 0.03592447 0.03592447 0.03592447 0.03592447 0.4737004 0.03592447
0.46631969 0.7289853 0.64143008 0.6414301 0.5538749 0.4663197 0.37876450 0.3787645 0.2912093 0.20365411 0.20365411 0.29120930 0.46631969 0.5538749 0.46631969
0.46631969 0.7289853 0.64143008 0.6414301 0.5538749 0.4663197 0.37876450 0.3787645 0.2912093 0.20365411 0.20365411 0.29120930 0.46631969 0.5538749 0.46631969
[,31]
y 0.03592447
0.46631969
0.46631969
[[1]]$labs
[1] "0\n\n7216 202\n100%" "0\n\n5131 26\n70%" "0\n\n2085 176\n30%" "0\n\n967 1\n13%" "0\n\n1118 175\n17%" "0\n\n672 18\n9%"
[7] "0\n\n446 157\n8%" "0\n\n329 42\n5%" "0\n\n248 12\n4%" "0\n\n81 30\n1%" "0\n\n57 2\n1%" "1\n\n24 28\n1%"
[13] "0\n\n24 21\n1%" "0\n\n22 12\n0%" "1\n\n2 9\n0%" "1\n\n0 7\n0%" "0\n\n117 115\n3%" "0\n\n49 4\n1%"
[19] "1\n\n68 111\n2%" "0\n\n53 37\n1%" "0\n\n52 28\n1%" "0\n\n12 0\n0%" "0\n\n40 28\n1%" "0\n\n35 16\n1%"
[25] "0\n\n29 7\n0%" "1\n\n6 9\n0%" "1\n\n5 12\n0%" "1\n\n1 9\n0%" "1\n\n15 74\n1%" "0\n\n7 4\n0%"
[31] "1\n\n8 70\n1%"
[[1]]$cex
[1] 0.175
[[1]]$boxes
[[1]]$boxes$x1
[1] 0.10946324 0.01804620 0.20088029 0.08282894 0.31893164 0.14761167 0.49025161 0.28122607 0.21239441 0.35005772 0.27717714 0.42293830 0.37435125 0.34195988
[15] 0.40674262 0.47152535 0.69927716 0.53630809 0.86224623 0.76709658 0.67397140 0.60109082 0.74685198 0.69826493 0.66587356 0.73065630 0.79543903 0.86022177
[29] 0.95739587 0.92500450 0.98978724
[[1]]$boxes$y1
[1] 0.98427011 0.02116297 0.89671492 0.02116297 0.80915973 0.02116297 0.72160453 0.63404934 0.02116297 0.54649414 0.02116297 0.45893895 0.37138375 0.02116297
[15] 0.02116297 0.02116297 0.63404934 0.02116297 0.54649414 0.45893895 0.37138375 0.02116297 0.28382856 0.19627336 0.02116297 0.02116297 0.02116297 0.02116297
[29] 0.45893895 0.02116297 0.02116297
[[1]]$boxes$x2
[1] 0.11568373 0.02426668 0.20710077 0.08904942 0.32515212 0.15383215 0.49647209 0.28744655 0.21861489 0.35627820 0.28339763 0.42915878 0.38057173 0.34818036
[15] 0.41296310 0.47774583 0.70549764 0.54252857 0.86846671 0.77331707 0.68019188 0.60731131 0.75307246 0.70448541 0.67209404 0.73687678 0.80165951 0.86644225
[29] 0.96361635 0.93122499 0.99600772
[[1]]$boxes$y2
[1] 0.99903161 0.03592447 0.91147641 0.03592447 0.82392122 0.03592447 0.73636602 0.64881083 0.03592447 0.56125563 0.03592447 0.47370044 0.38614525 0.03592447
[15] 0.03592447 0.03592447 0.64881083 0.03592447 0.56125563 0.47370044 0.38614525 0.03592447 0.29859005 0.21103486 0.03592447 0.03592447 0.03592447 0.03592447
[29] 0.47370044 0.03592447 0.03592447
[[1]]$split.labs
[1] ""
[[1]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[1]]$split.box
[[1]]$split.box$x1
[1] -0.0006152452 NA 0.0706989967 NA 0.1342376362 NA 0.2641197400 0.1996424204 NA 0.2445196143 NA
[12] 0.3603551638 0.3341842777 NA NA NA 0.5235561001 NA 0.7450138741 0.6661957994 0.5877167878 NA
[23] 0.6926664941 0.6497003069 NA NA NA NA 0.9134966121 NA NA
[[1]]$split.box$y1
[1] 0.9428641 NA 0.8553089 NA 0.7677537 NA 0.6801985 0.5926433 NA 0.5050881 NA 0.4175330 0.3299778 NA NA
[16] NA 0.5926433 NA 0.5050881 0.4175330 0.3299778 NA 0.2424226 0.1548674 NA NA NA NA 0.4175330 NA
[31] NA
[[1]]$split.box$x2
[1] 0.04292813 NA 0.10117936 NA 0.16720619 NA 0.30455287 0.23136688 NA 0.31605516 NA 0.39456781 0.35595596 NA
[15] NA NA 0.55528056 NA 0.79539978 0.68796749 0.62068534 NA 0.71008384 0.68826729 NA NA NA NA
[29] 0.94273288 NA NA
[[1]]$split.box$y2
[1] 0.9576256 NA 0.8700704 NA 0.7825152 NA 0.6949600 0.6074048 NA 0.5198496 NA 0.4322944 0.3447393 NA NA
[16] NA 0.6074048 NA 0.5198496 0.4322944 0.3447393 NA 0.2571841 0.1696289 NA NA NA NA 0.4322944 NA
[31] NA
[[2]]
[[2]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 1030 0 (0.861148558 0.138851442)
2) star_name_na=!NA 6638 302 0 (0.954504369 0.045495631)
4) orbital_period_na=!NA 6083 55 0 (0.990958409 0.009041591) *
5) orbital_period_na=NA 555 247 0 (0.554954955 0.445045045)
10) star_distance>=413.175 282 3 0 (0.989361702 0.010638298) *
11) star_distance< 413.175 273 29 1 (0.106227106 0.893772894) *
3) star_name_na=NA 780 52 1 (0.066666667 0.933333333)
6) star_distance>=947.5 28 0 0 (1.000000000 0.000000000) *
7) star_distance< 947.5 752 24 1 (0.031914894 0.968085106) *
[[2]]$snipped.nodes
NULL
[[2]]$xlim
[1] 0 1
[[2]]$ylim
[1] 0 1
[[2]]$x
[1] 0.55371072 0.24874130 0.08239435 0.41508826 0.30419029 0.52598623 0.85868014 0.74778217 0.96957811
[[2]]$y
[1] 0.92779006 0.66029712 0.04506335 0.39280417 0.04506335 0.04506335 0.66029712 0.04506335 0.04506335
[[2]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
x 0.5537107 0.2487413 0.08239435 0.4150883 0.3041903 0.5259862 0.8586801 0.7477822 0.9695781
NA 0.2487413 0.08239435 0.4150883 0.3041903 0.5259862 0.8586801 0.7477822 0.9695781
NA 0.5537107 0.24874130 0.2487413 0.4150883 0.4150883 0.5537107 0.8586801 0.8586801
[[2]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
y 1.003443 0.7359498 0.1207160 0.4684568 0.1207160 0.1207160 0.7359498 0.1207160 0.1207160
NA 0.9788402 0.7113473 0.7113473 0.4438543 0.4438543 0.9788402 0.7113473 0.7113473
NA 0.9788402 0.7113473 0.7113473 0.4438543 0.4438543 0.9788402 0.7113473 0.7113473
[[2]]$labs
[1] "0\n\n6388 1030\n100%" "0\n\n6336 302\n89%" "0\n\n6028 55\n82%" "0\n\n308 247\n7%" "0\n\n279 3\n4%" "1\n\n29 244\n4%"
[7] "1\n\n52 728\n11%" "0\n\n28 0\n0%" "1\n\n24 728\n10%"
[[2]]$cex
[1] 0.625
[[2]]$boxes
[[2]]$boxes$x1
[1] 0.5428249 0.2378555 0.0715085 0.4042024 0.2933044 0.5151004 0.8477943 0.7368963 0.9586923
[[2]]$boxes$y1
[1] 0.95423774 0.68674479 0.07151102 0.41925185 0.07151102 0.07151102 0.68674479 0.07151102 0.07151102
[[2]]$boxes$x2
[1] 0.56459656 0.25962715 0.09328019 0.42597410 0.31507613 0.53687207 0.86956598 0.75866801 0.98046395
[[2]]$boxes$y2
[1] 1.0034427 0.7359498 0.1207160 0.4684568 0.1207160 0.1207160 0.7359498 0.1207160 0.1207160
[[2]]$split.labs
[1] ""
[[2]]$split.cex
[1] 1 1 1 1 1 1 1 1 1
[[2]]$split.box
[[2]]$split.box$x1
[1] 0.17017662 -0.01047745 NA 0.22438151 NA NA 0.66797339 NA NA
[[2]]$split.box$y1
[1] 0.8136345 0.5461416 NA 0.2786486 NA NA 0.5461416 NA NA
[[2]]$split.box$x2
[1] 0.3273060 0.1752661 NA 0.3839991 NA NA 0.8275910 NA NA
[[2]]$split.box$y2
[1] 0.8628395 0.5953465 NA 0.3278536 NA NA 0.5953465 NA NA
[[3]]
[[3]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 1266 0 (0.829334052 0.170665948)
2) mass_sini_na=NA 6011 138 0 (0.977042090 0.022957910)
4) omega_na=NA 5264 27 0 (0.994870821 0.005129179) *
5) omega_na=!NA 747 111 0 (0.851405622 0.148594378)
10) radius_na=!NA 598 16 0 (0.973244147 0.026755853) *
11) radius_na=NA 149 54 1 (0.362416107 0.637583893)
22) k_na=NA 59 13 0 (0.779661017 0.220338983) *
23) k_na=!NA 90 8 1 (0.088888889 0.911111111) *
3) mass_sini_na=!NA 1407 279 1 (0.198294243 0.801705757)
6) star_distance>=228.4595 256 61 0 (0.761718750 0.238281250)
12) tperi_na=NA 208 21 0 (0.899038462 0.100961538) *
13) tperi_na=!NA 48 8 1 (0.166666667 0.833333333) *
7) star_distance< 228.4595 1151 84 1 (0.072980017 0.927019983)
14) impact_parameter_na=!NA 29 6 0 (0.793103448 0.206896552) *
15) impact_parameter_na=NA 1122 61 1 (0.054367201 0.945632799) *
[[3]]$snipped.nodes
NULL
[[3]]$xlim
[1] 0 1
[[3]]$ylim
[1] 0 1
[[3]]$x
[1] 0.46609815 0.15887906 0.04263399 0.27512412 0.17548549 0.37476275 0.30833700 0.44118850 0.77331725 0.64046575 0.57404000 0.70689150 0.90616875 0.83974300
[15] 0.97259450
[[3]]$y
[1] 0.94452006 0.73769510 0.03449025 0.53087015 0.03449025 0.32404519 0.03449025 0.03449025 0.73769510 0.53087015 0.03449025 0.03449025 0.53087015 0.03449025
[15] 0.03449025
[[3]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
x 0.4660982 0.1588791 0.04263399 0.2751241 0.1754855 0.3747627 0.3083370 0.4411885 0.7733172 0.6404657 0.5740400 0.7068915 0.9061687 0.8397430 0.9725945
NA 0.1588791 0.04263399 0.2751241 0.1754855 0.3747627 0.3083370 0.4411885 0.7733172 0.6404657 0.5740400 0.7068915 0.9061687 0.8397430 0.9725945
NA 0.4660982 0.15887906 0.1588791 0.2751241 0.2751241 0.3747627 0.3747627 0.4660982 0.7733172 0.6404657 0.6404657 0.7733172 0.9061687 0.9061687
[[3]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
y 1.000872 0.7940471 0.09084226 0.5872222 0.09084226 0.3803972 0.09084226 0.09084226 0.7940471 0.5872222 0.09084226 0.09084226 0.5872222 0.09084226 0.09084226
NA 0.9824202 0.77559524 0.7755952 0.56877028 0.5687703 0.36194533 0.36194533 0.9824202 0.7755952 0.56877028 0.56877028 0.7755952 0.56877028 0.56877028
NA 0.9824202 0.77559524 0.7755952 0.56877028 0.5687703 0.36194533 0.36194533 0.9824202 0.7755952 0.56877028 0.56877028 0.7755952 0.56877028 0.56877028
[[3]]$labs
[1] "0\n\n6152 1266\n100%" "0\n\n5873 138\n81%" "0\n\n5237 27\n71%" "0\n\n636 111\n10%" "0\n\n582 16\n8%" "1\n\n54 95\n2%"
[7] "0\n\n46 13\n1%" "1\n\n8 82\n1%" "1\n\n279 1128\n19%" "0\n\n195 61\n3%" "0\n\n187 21\n3%" "1\n\n8 40\n1%"
[13] "1\n\n84 1067\n16%" "0\n\n23 6\n0%" "1\n\n61 1061\n15%"
[[3]]$cex
[1] 0.4625
[[3]]$boxes
[[3]]$boxes$x1
[1] 0.45801153 0.15079243 0.03454737 0.26703749 0.16739887 0.36667612 0.30025037 0.43310187 0.76523062 0.63237912 0.56595337 0.69880487 0.89808212 0.83165637
[15] 0.96450787
[[3]]$boxes$y1
[1] 0.96396833 0.75714337 0.05393852 0.55031842 0.05393852 0.34349346 0.05393852 0.05393852 0.75714337 0.55031842 0.05393852 0.05393852 0.55031842 0.05393852
[15] 0.05393852
[[3]]$boxes$x2
[1] 0.47418478 0.16696568 0.05072062 0.28321075 0.18357212 0.38284937 0.31642362 0.44927512 0.78140387 0.64855237 0.58212662 0.71497812 0.91425537 0.84782962
[15] 0.98068112
[[3]]$boxes$y2
[1] 1.00087206 0.79404711 0.09084226 0.58722215 0.09084226 0.38039720 0.09084226 0.09084226 0.79404711 0.58722215 0.09084226 0.09084226 0.58722215 0.09084226
[15] 0.09084226
[[3]]$split.labs
[1] ""
[[3]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[3]]$split.box
[[3]]$split.box$x1
[1] 0.103392359 -0.003210957 NA 0.129329519 NA 0.278665297 NA NA 0.580002663 0.534726551 NA NA
[13] 0.759685396 NA NA
[[3]]$split.box$y1
[1] 0.8589034 0.6520784 NA 0.4452535 NA 0.2384285 NA NA 0.6520784 0.4452535 NA NA 0.4452535 NA NA
[[3]]$split.box$x2
[1] 0.21436576 0.08847895 NA 0.22164147 NA 0.33800869 NA NA 0.70092883 0.61335344 NA NA 0.91980060 NA
[15] NA
[[3]]$split.box$y2
[1] 0.8958071 0.6889822 NA 0.4821572 NA 0.2753323 NA NA 0.6889822 0.4821572 NA NA 0.4821572 NA NA
[[4]]
[[4]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 19 0 (0.997438663 0.002561337) *
[[4]]$snipped.nodes
NULL
[[4]]$xlim
[1] 0 1
[[4]]$ylim
[1] 0 1
[[4]]$x
[1] 0.5
[[4]]$y
[1] 0.5
[[4]]$branch.x
[,1]
x 0.5
NA
NA
[[4]]$branch.y
[,1]
y 0.5
NA
NA
[[4]]$labs
[1] "0\n\n7399 19\n100%"
[[4]]$cex
[1] 1
[[4]]$boxes
[[4]]$boxes$x1
[1] 0.4819606
[[4]]$boxes$y1
[1] 0.5429313
[[4]]$boxes$x2
[1] 0.5180394
[[4]]$boxes$y2
[1] 0.6191991
[[4]]$split.labs
[1] ""
[[4]]$split.cex
[1] 1
[[4]]$split.box
[[4]]$split.box$x1
[1] NA
[[4]]$split.box$y1
[1] NA
[[4]]$split.box$x2
[1] NA
[[4]]$split.box$y2
[1] NA
[[5]]
[[5]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 101 0 (0.986384470 0.013615530)
2) diff_disc_updated< 11.64617 6239 34 0 (0.994550409 0.005449591) *
3) diff_disc_updated>=11.64617 1179 67 0 (0.943172180 0.056827820)
6) star_distance< 143.995 791 16 0 (0.979772440 0.020227560) *
7) star_distance>=143.995 388 51 0 (0.868556701 0.131443299)
14) star_distance>=145.3505 353 31 0 (0.912181303 0.087818697)
28) ra>=84.77248 276 11 0 (0.960144928 0.039855072) *
29) ra< 84.77248 77 20 0 (0.740259740 0.259740260)
58) ra< 84.7573 64 7 0 (0.890625000 0.109375000) *
59) ra>=84.7573 13 0 1 (0.000000000 1.000000000) *
15) star_distance< 145.3505 35 15 1 (0.428571429 0.571428571)
30) temp_calculated_na=NA 14 2 0 (0.857142857 0.142857143) *
31) temp_calculated_na=!NA 21 3 1 (0.142857143 0.857142857) *
[[5]]$snipped.nodes
NULL
[[5]]$xlim
[1] 0 1
[[5]]$ylim
[1] 0 1
[[5]]$x
[1] 0.24782098 0.04721646 0.44842549 0.20378584 0.69306514 0.47778225 0.36035521 0.59520928 0.51692459 0.67349396 0.90834803 0.83006334 0.98663272
[[5]]$y
[1] 0.95519506 0.02769075 0.78655791 0.02769075 0.61792077 0.44928362 0.02769075 0.28064647 0.02769075 0.02769075 0.44928362 0.02769075 0.02769075
[[5]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
x 0.247821 0.04721646 0.4484255 0.2037858 0.6930651 0.4777822 0.3603552 0.5952093 0.5169246 0.6734940 0.9083480 0.8300633 0.9866327
NA 0.04721646 0.4484255 0.2037858 0.6930651 0.4777822 0.3603552 0.5952093 0.5169246 0.6734940 0.9083480 0.8300633 0.9866327
NA 0.24782098 0.2478210 0.4484255 0.4484255 0.6930651 0.4777822 0.4777822 0.5952093 0.5952093 0.6930651 0.9083480 0.9083480
[[5]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
y 0.9998117 0.07230737 0.8311745 0.07230737 0.6625374 0.4939002 0.07230737 0.3252631 0.07230737 0.07230737 0.4939002 0.07230737 0.07230737
NA 0.98505018 0.9850502 0.81641303 0.8164130 0.6477759 0.47913874 0.4791387 0.31050160 0.31050160 0.6477759 0.47913874 0.47913874
NA 0.98505018 0.9850502 0.81641303 0.8164130 0.6477759 0.47913874 0.4791387 0.31050160 0.31050160 0.6477759 0.47913874 0.47913874
[[5]]$labs
[1] "0\n\n7317 101\n100%" "0\n\n6205 34\n84%" "0\n\n1112 67\n16%" "0\n\n775 16\n11%" "0\n\n337 51\n5%" "0\n\n322 31\n5%"
[7] "0\n\n265 11\n4%" "0\n\n57 20\n1%" "0\n\n57 7\n1%" "1\n\n0 13\n0%" "1\n\n15 20\n0%" "0\n\n12 2\n0%"
[13] "1\n\n3 18\n0%"
[[5]]$cex
[1] 0.3625
[[5]]$boxes
[[5]]$boxes$x1
[1] 0.24191152 0.04130701 0.44251603 0.19787638 0.68715568 0.47187279 0.35444576 0.58929982 0.51101513 0.66758451 0.90243857 0.82415388 0.98072326
[[5]]$boxes$y1
[1] 0.97028869 0.04278438 0.80165154 0.04278438 0.63301439 0.46437725 0.04278438 0.29574010 0.04278438 0.04278438 0.46437725 0.04278438 0.04278438
[[5]]$boxes$x2
[1] 0.25373043 0.05312592 0.45433495 0.20969530 0.69897459 0.48369170 0.36626467 0.60111873 0.52283405 0.67940342 0.91425749 0.83597280 0.99254217
[[5]]$boxes$y2
[1] 0.99981168 0.07230737 0.83117453 0.07230737 0.66253738 0.49390024 0.07230737 0.32526309 0.07230737 0.07230737 0.49390024 0.07230737 0.07230737
[[5]]$split.labs
[1] ""
[[5]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1
[[5]]$split.box
[[5]]$split.box$x1
[1] -0.00304503 NA 0.16067790 NA 0.43187509 0.34212920 NA 0.50149779 NA NA 0.77420341 NA NA
[[5]]$split.box$y1
[1] 0.8867017 NA 0.7180646 NA 0.5494274 0.3807903 NA 0.2121531 NA NA 0.3807903 NA NA
[[5]]$split.box$x2
[1] 0.09747796 NA 0.24689378 NA 0.52368940 0.37858123 NA 0.53235138 NA NA 0.88592327 NA NA
[[5]]$split.box$y2
[1] 0.9162247 NA 0.7475876 NA 0.5789504 0.4103133 NA 0.2416761 NA NA 0.4103133 NA NA
[[6]]
[[6]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 2890 1 (0.38959288 0.61040712)
2) radius_na=NA 2444 43 0 (0.98240589 0.01759411) *
3) radius_na=!NA 4974 489 1 (0.09831122 0.90168878)
6) orbital_period_na=NA 347 10 0 (0.97118156 0.02881844) *
7) orbital_period_na=!NA 4627 152 1 (0.03285066 0.96714934)
14) discovered< 2006.5 64 17 0 (0.73437500 0.26562500) *
15) discovered>=2006.5 4563 105 1 (0.02301118 0.97698882) *
[[6]]$snipped.nodes
NULL
[[6]]$xlim
[1] 0 1
[[6]]$ylim
[1] 0 1
[[6]]$x
[1] 0.3189600 0.0545808 0.5833392 0.3567285 0.8099500 0.6588761 0.9610238
[[6]]$y
[1] 0.92779006 0.04506335 0.66029712 0.04506335 0.39280417 0.04506335 0.04506335
[[6]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
x 0.31896 0.0545808 0.5833392 0.3567285 0.8099500 0.6588761 0.9610238
NA 0.0545808 0.5833392 0.3567285 0.8099500 0.6588761 0.9610238
NA 0.3189600 0.3189600 0.5833392 0.5833392 0.8099500 0.8099500
[[6]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
y 1.003443 0.1207160 0.7359498 0.1207160 0.4684568 0.1207160 0.1207160
NA 0.9788402 0.9788402 0.7113473 0.7113473 0.4438543 0.4438543
NA 0.9788402 0.9788402 0.7113473 0.7113473 0.4438543 0.4438543
[[6]]$labs
[1] "1\n\n2890 4528\n100%" "0\n\n2401 43\n33%" "1\n\n489 4485\n67%" "0\n\n337 10\n5%" "1\n\n152 4475\n62%" "0\n\n47 17\n1%"
[7] "1\n\n105 4458\n62%"
[[6]]$cex
[1] 0.625
[[6]]$boxes
[[6]]$boxes$x1
[1] 0.30807416 0.04369495 0.57245336 0.34584262 0.79906411 0.64799028 0.95013794
[[6]]$boxes$y1
[1] 0.95423774 0.07151102 0.68674479 0.07151102 0.41925185 0.07151102 0.07151102
[[6]]$boxes$x2
[1] 0.32984585 0.06546664 0.59422505 0.36761430 0.82083580 0.66976197 0.97190963
[[6]]$boxes$y2
[1] 1.0034427 0.1207160 0.7359498 0.1207160 0.4684568 0.1207160 0.1207160
[[6]]$split.labs
[1] ""
[[6]]$split.cex
[1] 1 1 1 1 1 1 1
[[6]]$split.box
[[6]]$split.box$x1
[1] -0.005633467 NA 0.266344858 NA 0.588398063 NA NA
[[6]]$split.box$y1
[1] 0.8136345 NA 0.5461416 NA 0.2786486 NA NA
[[6]]$split.box$x2
[1] 0.1147951 NA 0.4471121 NA 0.7293542 NA NA
[[6]]$split.box$y2
[1] 0.8628395 NA 0.5953465 NA 0.3278536 NA NA
[[7]]
[[7]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 313 0 (0.9578053384 0.0421946616)
2) star_distance< 2644 7095 65 0 (0.9908386187 0.0091613813)
4) orbital_period_na=!NA 6048 2 0 (0.9996693122 0.0003306878) *
5) orbital_period_na=NA 1047 63 0 (0.9398280802 0.0601719198)
10) star_distance< 908 1018 39 0 (0.9616895874 0.0383104126)
20) updated>=2024.141 894 5 0 (0.9944071588 0.0055928412) *
21) updated< 2024.141 124 34 0 (0.7258064516 0.2741935484)
42) ra< 256.95 82 1 0 (0.9878048780 0.0121951220) *
43) ra>=256.95 42 9 1 (0.2142857143 0.7857142857)
86) ra>=276.3813 9 0 0 (1.0000000000 0.0000000000) *
87) ra< 276.3813 33 0 1 (0.0000000000 1.0000000000) *
11) star_distance>=908 29 5 1 (0.1724137931 0.8275862069) *
3) star_distance>=2644 323 75 1 (0.2321981424 0.7678018576)
6) orbital_period_na=!NA 77 6 0 (0.9220779221 0.0779220779) *
7) orbital_period_na=NA 246 4 1 (0.0162601626 0.9837398374) *
[[7]]$snipped.nodes
NULL
[[7]]$xlim
[1] 0 1
[[7]]$ylim
[1] 0 1
[[7]]$x
[1] 0.59552940 0.27204608 0.03947638 0.50461579 0.29318878 0.17478966 0.41158791 0.31010294 0.51307287 0.44541623 0.58072951 0.71604279 0.91901272 0.85135607
[15] 0.98666936
[[7]]$y
[1] 0.96299192 0.82050031 0.02254728 0.67800870 0.53551708 0.02254728 0.39302547 0.02254728 0.25053386 0.02254728 0.02254728 0.02254728 0.82050031 0.02254728
[15] 0.02254728
[[7]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
x 0.5955294 0.2720461 0.03947638 0.5046158 0.2931888 0.1747897 0.4115879 0.3101029 0.5130729 0.4454162 0.5807295 0.7160428 0.9190127 0.8513561 0.9866694
NA 0.2720461 0.03947638 0.5046158 0.2931888 0.1747897 0.4115879 0.3101029 0.5130729 0.4454162 0.5807295 0.7160428 0.9190127 0.8513561 0.9866694
NA 0.5955294 0.27204608 0.2720461 0.5046158 0.2931888 0.2931888 0.4115879 0.4115879 0.5130729 0.5130729 0.5046158 0.5955294 0.9190127 0.9190127
[[7]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
y 0.9992683 0.8567767 0.05882365 0.7142851 0.5717935 0.05882365 0.4293018 0.05882365 0.2868102 0.05882365 0.05882365 0.05882365 0.8567767 0.05882365 0.05882365
NA 0.9869670 0.84447543 0.8444754 0.7019838 0.55949221 0.5594922 0.41700060 0.4170006 0.27450899 0.27450899 0.70198382 0.9869670 0.84447543 0.84447543
NA 0.9869670 0.84447543 0.8444754 0.7019838 0.55949221 0.5594922 0.41700060 0.4170006 0.27450899 0.27450899 0.70198382 0.9869670 0.84447543 0.84447543
[[7]]$labs
[1] "0\n\n7105 313\n100%" "0\n\n7030 65\n96%" "0\n\n6046 2\n82%" "0\n\n984 63\n14%" "0\n\n979 39\n14%" "0\n\n889 5\n12%"
[7] "0\n\n90 34\n2%" "0\n\n81 1\n1%" "1\n\n9 33\n1%" "0\n\n9 0\n0%" "1\n\n0 33\n0%" "1\n\n5 24\n0%"
[13] "1\n\n75 248\n4%" "0\n\n71 6\n1%" "1\n\n4 242\n3%"
[[7]]$cex
[1] 0.2875
[[7]]$boxes
[[7]]$boxes$x1
[1] 0.59055301 0.26706970 0.03449999 0.49963940 0.28821240 0.16981328 0.40661152 0.30512656 0.50809648 0.44043984 0.57575312 0.71106641 0.91403633 0.84637969
[15] 0.98169297
[[7]]$boxes$y1
[1] 0.97466580 0.83217419 0.03422116 0.68968258 0.54719097 0.03422116 0.40469935 0.03422116 0.26220774 0.03422116 0.03422116 0.03422116 0.83217419 0.03422116
[15] 0.03422116
[[7]]$boxes$x2
[1] 0.60050578 0.27702247 0.04445276 0.50959217 0.29816517 0.17976605 0.41656429 0.31507933 0.51804925 0.45039261 0.58570589 0.72101918 0.92398910 0.85633246
[15] 0.99164574
[[7]]$boxes$y2
[1] 0.99926829 0.85677668 0.05882365 0.71428507 0.57179346 0.05882365 0.42930184 0.05882365 0.28681023 0.05882365 0.05882365 0.05882365 0.85677668 0.05882365
[15] 0.05882365
[[7]]$split.labs
[1] ""
[[7]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[7]]$split.box
[[7]]$split.box$x1
[1] 0.23410114 -0.00220085 NA 0.25742101 0.14493135 NA 0.29517379 NA 0.42830990 NA NA NA 0.80967885
[14] NA NA
[[7]]$split.box$y1
[1] 0.9059141 0.7634225 NA 0.6209309 0.4784393 NA 0.3359477 NA 0.1934561 NA NA NA 0.7634225 NA NA
[[7]]$split.box$x2
[1] 0.30999102 0.08115361 NA 0.32895655 0.20464797 NA 0.32503210 NA 0.46252255 NA NA NA 0.89303330 NA
[15] NA
[[7]]$split.box$y2
[1] 0.9305166 0.7880250 NA 0.6455334 0.5030418 NA 0.3605502 NA 0.2180586 NA NA NA 0.7880250 NA NA
[[8]]
[[8]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 34 0 (0.995416554 0.004583446)
2) mass_measurement_type_na=NA 4320 6 0 (0.998611111 0.001388889) *
3) mass_measurement_type_na=!NA 3098 28 0 (0.990961911 0.009038089)
6) is_kepler< 0.5 2916 9 0 (0.996913580 0.003086420) *
7) is_kepler>=0.5 182 19 0 (0.895604396 0.104395604)
14) tconj_na=!NA 111 0 0 (1.000000000 0.000000000) *
15) tconj_na=NA 71 19 0 (0.732394366 0.267605634)
30) updated>=2021.458 48 7 0 (0.854166667 0.145833333) *
31) updated< 2021.458 23 11 1 (0.478260870 0.521739130)
62) mag_v_na=!NA 14 4 0 (0.714285714 0.285714286) *
63) mag_v_na=NA 9 1 1 (0.111111111 0.888888889) *
[[8]]$snipped.nodes
NULL
[[8]]$xlim
[1] 0 1
[[8]]$ylim
[1] 0 1
[[8]]$x
[1] 0.24761363 0.06940282 0.42582443 0.25336236 0.59828649 0.43732190 0.75925109 0.62128143 0.89722074 0.80524097 0.98920051
[[8]]$y
[1] 0.95519506 0.02769075 0.78655791 0.02769075 0.61792077 0.02769075 0.44928362 0.02769075 0.28064647 0.02769075 0.02769075
[[8]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
x 0.2476136 0.06940282 0.4258244 0.2533624 0.5982865 0.4373219 0.7592511 0.6212814 0.8972207 0.8052410 0.9892005
NA 0.06940282 0.4258244 0.2533624 0.5982865 0.4373219 0.7592511 0.6212814 0.8972207 0.8052410 0.9892005
NA 0.24761363 0.2476136 0.4258244 0.4258244 0.5982865 0.5982865 0.7592511 0.7592511 0.8972207 0.8972207
[[8]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
y 0.9998117 0.07230737 0.8311745 0.07230737 0.6625374 0.07230737 0.4939002 0.07230737 0.3252631 0.07230737 0.07230737
NA 0.98505018 0.9850502 0.81641303 0.8164130 0.64777589 0.6477759 0.47913874 0.4791387 0.31050160 0.31050160
NA 0.98505018 0.9850502 0.81641303 0.8164130 0.64777589 0.6477759 0.47913874 0.4791387 0.31050160 0.31050160
[[8]]$labs
[1] "0\n\n7384 34\n100%" "0\n\n4314 6\n58%" "0\n\n3070 28\n42%" "0\n\n2907 9\n39%" "0\n\n163 19\n2%" "0\n\n111 0\n1%" "0\n\n52 19\n1%"
[8] "0\n\n41 7\n1%" "1\n\n11 12\n0%" "0\n\n10 4\n0%" "1\n\n1 8\n0%"
[[8]]$cex
[1] 0.3625
[[8]]$boxes
[[8]]$boxes$x1
[1] 0.24170417 0.06349337 0.41991497 0.24745290 0.59237703 0.43141244 0.75334163 0.61537198 0.89131128 0.79933151 0.98329105
[[8]]$boxes$y1
[1] 0.97028869 0.04278438 0.80165154 0.04278438 0.63301439 0.04278438 0.46437725 0.04278438 0.29574010 0.04278438 0.04278438
[[8]]$boxes$x2
[1] 0.25352308 0.07531228 0.43173388 0.25927182 0.60419595 0.44323136 0.76516055 0.62719089 0.90313020 0.81115043 0.99510997
[[8]]$boxes$y2
[1] 0.99981168 0.07230737 0.83117453 0.07230737 0.66253738 0.07230737 0.49390024 0.07230737 0.32526309 0.07230737 0.07230737
[[8]]$split.labs
[1] ""
[[8]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1
[[8]]$split.box
[[8]]$split.box$x1
[1] -0.006051621 NA 0.224561530 NA 0.405099802 NA 0.584393977 NA 0.769597611 NA NA
[[8]]$split.box$y1
[1] 0.8867017 NA 0.7180646 NA 0.5494274 NA 0.3807903 NA 0.2121531 NA NA
[[8]]$split.box$x2
[1] 0.1448573 NA 0.2821632 NA 0.4695440 NA 0.6581689 NA 0.8408843 NA NA
[[8]]$split.box$y2
[1] 0.9162247 NA 0.7475876 NA 0.5789504 NA 0.4103133 NA 0.2416761 NA NA
[[9]]
[[9]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 178 0 (0.976004314 0.023995686)
2) star_distance< 2199.205 7073 111 0 (0.984306518 0.015693482)
4) star_metallicity_na=!NA 5214 12 0 (0.997698504 0.002301496) *
5) star_metallicity_na=NA 1859 99 0 (0.946745562 0.053254438)
10) mass_sini_na=NA 1593 33 0 (0.979284369 0.020715631)
20) tperi_na=NA 1569 25 0 (0.984066284 0.015933716) *
21) tperi_na=!NA 24 8 0 (0.666666667 0.333333333)
42) star_distance< 123.45 14 0 0 (1.000000000 0.000000000) *
43) star_distance>=123.45 10 2 1 (0.200000000 0.800000000) *
11) mass_sini_na=!NA 266 66 0 (0.751879699 0.248120301)
22) star_distance< 207.1075 191 6 0 (0.968586387 0.031413613) *
23) star_distance>=207.1075 75 15 1 (0.200000000 0.800000000)
46) diff_disc_updated< 0.4678569 8 2 0 (0.750000000 0.250000000) *
47) diff_disc_updated>=0.4678569 67 9 1 (0.134328358 0.865671642) *
3) star_distance>=2199.205 345 67 0 (0.805797101 0.194202899)
6) orbital_period_na=NA 249 0 0 (1.000000000 0.000000000) *
7) orbital_period_na=!NA 96 29 1 (0.302083333 0.697916667)
14) star_metallicity_na=!NA 18 2 0 (0.888888889 0.111111111) *
15) star_metallicity_na=NA 78 13 1 (0.166666667 0.833333333)
30) angular_distance_na=!NA 7 1 0 (0.857142857 0.142857143) *
31) angular_distance_na=NA 71 7 1 (0.098591549 0.901408451) *
[[9]]$snipped.nodes
NULL
[[9]]$xlim
[1] 0 1
[[9]]$ylim
[1] 0 1
[[9]]$x
[1] 0.49520907 0.20266719 0.05054542 0.35478897 0.21436887 0.14415882 0.28457892 0.23777222 0.33138562 0.49520907 0.42499902 0.56541912 0.51861242 0.61222582
[15] 0.78775094 0.70583922 0.86966267 0.79945262 0.93987272 0.89306602 0.98667942
[[9]]$y
[1] 0.95519506 0.78655791 0.02769075 0.61792077 0.44928362 0.02769075 0.28064647 0.02769075 0.02769075 0.44928362 0.02769075 0.28064647 0.02769075 0.02769075
[15] 0.78655791 0.02769075 0.61792077 0.02769075 0.44928362 0.02769075 0.02769075
[[9]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
x 0.4952091 0.2026672 0.05054542 0.3547890 0.2143689 0.1441588 0.2845789 0.2377722 0.3313856 0.4952091 0.4249990 0.5654191 0.5186124 0.6122258 0.7877509
NA 0.2026672 0.05054542 0.3547890 0.2143689 0.1441588 0.2845789 0.2377722 0.3313856 0.4952091 0.4249990 0.5654191 0.5186124 0.6122258 0.7877509
NA 0.4952091 0.20266719 0.2026672 0.3547890 0.2143689 0.2143689 0.2845789 0.2845789 0.3547890 0.4952091 0.4952091 0.5654191 0.5654191 0.4952091
[,16] [,17] [,18] [,19] [,20] [,21]
x 0.7058392 0.8696627 0.7994526 0.9398727 0.8930660 0.9866794
0.7058392 0.8696627 0.7994526 0.9398727 0.8930660 0.9866794
0.7877509 0.7877509 0.8696627 0.8696627 0.9398727 0.9398727
[[9]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
y 0.9998117 0.8311745 0.07230737 0.6625374 0.4939002 0.07230737 0.3252631 0.07230737 0.07230737 0.4939002 0.07230737 0.3252631 0.07230737 0.07230737 0.8311745
NA 0.9850502 0.81641303 0.8164130 0.6477759 0.47913874 0.4791387 0.31050160 0.31050160 0.6477759 0.47913874 0.4791387 0.31050160 0.31050160 0.9850502
NA 0.9850502 0.81641303 0.8164130 0.6477759 0.47913874 0.4791387 0.31050160 0.31050160 0.6477759 0.47913874 0.4791387 0.31050160 0.31050160 0.9850502
[,16] [,17] [,18] [,19] [,20] [,21]
y 0.07230737 0.6625374 0.07230737 0.4939002 0.07230737 0.07230737
0.81641303 0.8164130 0.64777589 0.6477759 0.47913874 0.47913874
0.81641303 0.8164130 0.64777589 0.6477759 0.47913874 0.47913874
[[9]]$labs
[1] "0\n\n7240 178\n100%" "0\n\n6962 111\n95%" "0\n\n5202 12\n70%" "0\n\n1760 99\n25%" "0\n\n1560 33\n21%" "0\n\n1544 25\n21%"
[7] "0\n\n16 8\n0%" "0\n\n14 0\n0%" "1\n\n2 8\n0%" "0\n\n200 66\n4%" "0\n\n185 6\n3%" "1\n\n15 60\n1%"
[13] "0\n\n6 2\n0%" "1\n\n9 58\n1%" "0\n\n278 67\n5%" "0\n\n249 0\n3%" "1\n\n29 67\n1%" "0\n\n16 2\n0%"
[19] "1\n\n13 65\n1%" "0\n\n6 1\n0%" "1\n\n7 64\n1%"
[[9]]$cex
[1] 0.3625
[[9]]$boxes
[[9]]$boxes$x1
[1] 0.48929961 0.19675773 0.04463596 0.34887951 0.20845941 0.13824936 0.27866946 0.23186276 0.32547616 0.48929961 0.41908956 0.55950966 0.51270296 0.60631636
[15] 0.78184149 0.69992976 0.86375321 0.79354316 0.93396326 0.88715656 0.98076996
[[9]]$boxes$y1
[1] 0.97028869 0.80165154 0.04278438 0.63301439 0.46437725 0.04278438 0.29574010 0.04278438 0.04278438 0.46437725 0.04278438 0.29574010 0.04278438 0.04278438
[15] 0.80165154 0.04278438 0.63301439 0.04278438 0.46437725 0.04278438 0.04278438
[[9]]$boxes$x2
[1] 0.50111853 0.20857665 0.05645487 0.36069842 0.22027832 0.15006827 0.29048837 0.24368167 0.33729507 0.50111853 0.43090848 0.57132858 0.52452188 0.61813528
[15] 0.79366040 0.71174868 0.87557213 0.80536208 0.94578218 0.89897548 0.99258888
[[9]]$boxes$y2
[1] 0.99981168 0.83117453 0.07230737 0.66253738 0.49390024 0.07230737 0.32526309 0.07230737 0.07230737 0.49390024 0.07230737 0.32526309 0.07230737 0.07230737
[15] 0.83117453 0.07230737 0.66253738 0.07230737 0.49390024 0.07230737 0.07230737
[[9]]$split.labs
[1] ""
[[9]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[9]]$split.box
[[9]]$split.box$x1
[1] 0.157071059 -0.003448367 NA 0.172816048 0.113802865 NA 0.194664277 NA NA 0.381891078 NA 0.464618635
[13] NA NA 0.654955677 NA 0.745458836 NA 0.834095851 NA NA
[[9]]$split.box$y1
[1] 0.8867017 0.7180646 NA 0.5494274 0.3807903 NA 0.2121531 NA NA 0.3807903 NA 0.2121531 NA NA 0.7180646
[16] NA 0.5494274 NA 0.3807903 NA NA
[[9]]$split.box$x2
[1] 0.2482633 0.1045392 NA 0.2559217 0.1745148 NA 0.2808802 NA NA 0.4681070 NA 0.5726062 NA NA 0.7567228
[16] NA 0.8534464 NA 0.9520362 NA NA
[[9]]$split.box$y2
[1] 0.9162247 0.7475876 NA 0.5789504 0.4103133 NA 0.2416761 NA NA 0.4103133 NA 0.2416761 NA NA 0.7475876
[16] NA 0.5789504 NA 0.4103133 NA NA
# get accuracy
accuracy <- function(y_true, y_pred) {
result <- list()
for (col in names(y_true)) {
confusion_matrix <- table(y_true[[col]], y_pred[[paste0("pred_", col)]])
result[[col]] <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
}
return(result)
}
accuracy(shadow_exoplanets_with_preds %>% select(starts_with("detection_type_")), predictions %>% select(starts_with("pred_")))$detection_type_astrometry
[1] 0.9854408
$detection_type_imaging
[1] 0.9850364
$detection_type_radial_velocity
[1] 0.9784308
$detection_type_kinematic
[1] 0.9974387
$detection_type_other
[1] 0.9901591
$detection_type_primary_transit
[1] 0.9764087
$detection_type_microlensing
[1] 0.9968994
$detection_type_ttv
[1] 0.9963602
$detection_type_timing
[1] 0.9911027set.seed(123)
shadow_exoplanets %>%
select(-name, -starts_with("detection_type"), -ends_with("_na"), -discovered, -updated, -diff_disc_updated, -is_kepler) %>%
as_shadow() %>%
bind_cols(shadow_exoplanets) %>%
select(-discovered, -updated, -diff_disc_updated, -is_kepler, -star_distance, -ra, -dec) %>%
relocate(name, starts_with("detection_type")) -> shadower_exoplanets
shadower_exoplanets %>% glimpseRows: 7,418
Columns: 54
$ name <chr> "109 Psc b", "112 Psc b", "112 Psc c", "11 Com Ab", "11 UMi b", "14 And Ab", "14 Her b", "14 Her c", "16 Cyg Bb", "18 Del…
$ detection_type_astrometry <fct> 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ detection_type_imaging <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ detection_type_radial_velocity <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ detection_type_kinematic <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ detection_type_other <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ detection_type_primary_transit <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ detection_type_microlensing <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ detection_type_ttv <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ detection_type_timing <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ star_distance_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA,…
$ ra_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ dec_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ mass_na <fct> !NA, NA, !NA, NA, NA, NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, !NA, !NA, …
$ mass_sini_na <fct> !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, NA, …
$ radius_na <fct> !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, !NA, NA, !NA,…
$ orbital_period_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, NA,…
$ semi_major_axis_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, NA…
$ eccentricity_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, !NA, NA, !…
$ inclination_na <fct> !NA, NA, !NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, …
$ angular_distance_na <fct> !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, !NA, !NA, NA, !NA, !NA, NA, NA, !NA, NA, NA, NA,…
$ omega_na <fct> !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, !NA…
$ tperi_na <fct> !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, …
$ tconj_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_tr_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_tr_sec_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ lambda_angle_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ impact_parameter_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ tzero_vr_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ k_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, !NA, NA, NA…
$ temp_calculated_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, NA, NA, !NA, !N…
$ temp_measured_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA,…
$ geometric_albedo_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ log_g_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA,…
$ mass_measurement_type_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, …
$ radius_measurement_type_na <fct> !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, !NA, !NA, NA, NA, !…
$ alternate_names_na <fct> !NA, !NA, !NA, NA, NA, NA, NA, !NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, NA, NA, NA, !NA, !NA, !NA, NA, !NA, !NA…
$ molecules_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, !NA, NA, NA, NA, NA, NA, NA…
$ star_name_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA,…
$ ra_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ dec_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !N…
$ mag_v_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, N…
$ mag_i_na <fct> NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, !NA, NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
$ mag_j_na <fct> NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, N…
$ mag_h_na <fct> NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, N…
$ mag_k_na <fct> NA, !NA, !NA, NA, NA, !NA, NA, NA, NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, NA, N…
$ star_metallicity_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA, NA…
$ star_mass_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, …
$ star_radius_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, N…
$ star_sp_type_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, N…
$ star_age_na <fct> !NA, NA, NA, NA, !NA, NA, !NA, !NA, !NA, NA, !NA, NA, !NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, NA, NA…
$ star_teff_na <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, N…
$ star_detected_disc_na <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ star_alternate_names_na <fct> !NA, !NA, !NA, !NA, NA, NA, NA, NA, !NA, !NA, !NA, !NA, !NA, NA, !NA, NA, !NA, !NA, NA, NA, NA, !NA, NA, NA, NA, NA, NA, …# Do the same thing with shadower_exoplanets
# Define target and predictor columns
target_cols <- names(shadower_exoplanets) %>%
keep(~ startsWith(.x, "detection_type_"))
predictor_cols <- names(shadower_exoplanets) %>%
setdiff(c("name", target_cols))
# Train decision trees for each target label
models <- target_cols %>%
set_names() %>%
map(~ rpart(as.formula(paste(.x, "~", paste(predictor_cols, collapse = " + "))),
data = shadower_exoplanets, method = "class"))
# Make predictions and add them to the original dataset
shadower_exoplanets_with_preds <- shadower_exoplanets %>%
bind_cols(models %>%
map_dfc(~ predict(.x, shadower_exoplanets, type = "class")) %>%
rename_with(~ paste0("pred_", target_cols)) # Prefix predictions for clarity
)
predictions <- shadower_exoplanets_with_preds %>%
mutate(
actual_combined = apply(select(., all_of(target_cols)), 1, paste, collapse = "_"),
predicted_combined = apply(select(., starts_with("pred_")), 1, paste, collapse = "_")
) %>% select(actual_combined, predicted_combined, starts_with("pred_"), starts_with("detection_type_"))
predictions# accuracy
accuracy(shadower_exoplanets_with_preds %>% select(starts_with("detection_type_")), predictions %>% select(starts_with("pred_")))$detection_type_astrometry
[1] 0.9791049
$detection_type_imaging
[1] 0.9766783
$detection_type_radial_velocity
[1] 0.9800485
$detection_type_kinematic
[1] 0.9974387
$detection_type_other
[1] 0.9863845
$detection_type_primary_transit
[1] 0.9723645
$detection_type_microlensing
[1] 0.9912375
$detection_type_ttv
[1] 0.995821
$detection_type_timing
[1] 0.9889458# plot
target_cols %>%
map2(models, ~ {
rpart.plot(.y,
type = 4,
extra = 101,
under = TRUE,
fallen.leaves = TRUE,
main = paste("Decision Tree for", .x)) # Title with the target label
})
[[1]]
[[1]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 202 0 (0.972768940 0.027231060)
2) radius_na=!NA 4974 21 0 (0.995778046 0.004221954) *
3) radius_na=NA 2444 181 0 (0.925941080 0.074058920)
6) inclination_na=NA 2146 52 0 (0.975768872 0.024231128) *
7) inclination_na=!NA 298 129 0 (0.567114094 0.432885906)
14) mag_k_na=NA 198 62 0 (0.686868687 0.313131313) *
15) mag_k_na=!NA 100 33 1 (0.330000000 0.670000000)
30) mass_measurement_type_na=NA 7 0 0 (1.000000000 0.000000000) *
31) mass_measurement_type_na=!NA 93 26 1 (0.279569892 0.720430108)
62) alternate_names_na=!NA 29 14 1 (0.482758621 0.517241379)
124) mass_sini_na=!NA 10 2 0 (0.800000000 0.200000000) *
125) mass_sini_na=NA 19 6 1 (0.315789474 0.684210526) *
63) alternate_names_na=NA 64 12 1 (0.187500000 0.812500000) *
[[1]]$snipped.nodes
NULL
[[1]]$xlim
[1] 0 1
[[1]]$ylim
[1] 0 1
[[1]]$x
[1] 0.18954181 0.02715255 0.35193106 0.18704351 0.51681862 0.34693447 0.68670277 0.50682543 0.86658010 0.74666188 0.66671640 0.82660736 0.98649832
[[1]]$y
[1] 0.96299192 0.02254728 0.82050031 0.02254728 0.67800870 0.02254728 0.53551708 0.02254728 0.39302547 0.25053386 0.02254728 0.02254728 0.02254728
[[1]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
x 0.1895418 0.02715255 0.3519311 0.1870435 0.5168186 0.3469345 0.6867028 0.5068254 0.8665801 0.7466619 0.6667164 0.8266074 0.9864983
NA 0.02715255 0.3519311 0.1870435 0.5168186 0.3469345 0.6867028 0.5068254 0.8665801 0.7466619 0.6667164 0.8266074 0.9864983
NA 0.18954181 0.1895418 0.3519311 0.3519311 0.5168186 0.5168186 0.6867028 0.6867028 0.8665801 0.7466619 0.7466619 0.8665801
[[1]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
y 0.9992683 0.05882365 0.8567767 0.05882365 0.7142851 0.05882365 0.5717935 0.05882365 0.4293018 0.2868102 0.05882365 0.05882365 0.05882365
NA 0.98696705 0.9869670 0.84447543 0.8444754 0.70198382 0.7019838 0.55949221 0.5594922 0.4170006 0.27450899 0.27450899 0.41700060
NA 0.98696705 0.9869670 0.84447543 0.8444754 0.70198382 0.7019838 0.55949221 0.5594922 0.4170006 0.27450899 0.27450899 0.41700060
[[1]]$labs
[1] "0\n\n7216 202\n100%" "0\n\n4953 21\n67%" "0\n\n2263 181\n33%" "0\n\n2094 52\n29%" "0\n\n169 129\n4%" "0\n\n136 62\n3%"
[7] "1\n\n33 67\n1%" "0\n\n7 0\n0%" "1\n\n26 67\n1%" "1\n\n14 15\n0%" "0\n\n8 2\n0%" "1\n\n6 13\n0%"
[13] "1\n\n12 52\n1%"
[[1]]$cex
[1] 0.2875
[[1]]$boxes
[[1]]$boxes$x1
[1] 0.18456542 0.02217616 0.34695468 0.18206712 0.51184223 0.34195809 0.68172638 0.50184905 0.86160371 0.74168549 0.66174001 0.82163097 0.98152194
[[1]]$boxes$y1
[1] 0.97466580 0.03422116 0.83217419 0.03422116 0.68968258 0.03422116 0.54719097 0.03422116 0.40469935 0.26220774 0.03422116 0.03422116 0.03422116
[[1]]$boxes$x2
[1] 0.19451819 0.03212893 0.35690745 0.19201990 0.52179501 0.35191086 0.69167915 0.51180182 0.87155648 0.75163826 0.67169278 0.83158374 0.99147471
[[1]]$boxes$y2
[1] 0.99926829 0.05882365 0.85677668 0.05882365 0.71428507 0.05882365 0.57179346 0.05882365 0.42930184 0.28681023 0.05882365 0.05882365 0.05882365
[[1]]$split.labs
[1] ""
[[1]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1
[[1]]$split.box
[[1]]$split.box$x1
[1] -0.001150644 NA 0.152830860 NA 0.318631280 NA 0.443998568 NA 0.699386215 0.631259650 NA NA
[13] NA
[[1]]$split.box$y1
[1] 0.9059141 NA 0.7634225 NA 0.6209309 NA 0.4784393 NA 0.3359477 0.1934561 NA NA NA
[[1]]$split.box$x2
[1] 0.05545574 NA 0.22125616 NA 0.37523766 NA 0.56965230 NA 0.79393754 0.70217314 NA NA NA
[[1]]$split.box$y2
[1] 0.9305166 NA 0.7880250 NA 0.6455334 NA 0.5030418 NA 0.3605502 0.2180586 NA NA NA
[[2]]
[[2]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 1030 0 (0.861148558 0.138851442)
2) star_name_na=!NA 6638 302 0 (0.954504369 0.045495631)
4) orbital_period_na=!NA 6083 55 0 (0.990958409 0.009041591) *
5) orbital_period_na=NA 555 247 0 (0.554954955 0.445045045)
10) star_sp_type_na=NA 262 10 0 (0.961832061 0.038167939) *
11) star_sp_type_na=!NA 293 56 1 (0.191126280 0.808873720) *
3) star_name_na=NA 780 52 1 (0.066666667 0.933333333) *
[[2]]$snipped.nodes
NULL
[[2]]$xlim
[1] 0 1
[[2]]$ylim
[1] 0 1
[[2]]$x
[1] 0.63688420 0.30419029 0.08239435 0.52598623 0.37812227 0.67385019 0.96957811
[[2]]$y
[1] 0.92779006 0.66029712 0.04506335 0.39280417 0.04506335 0.04506335 0.04506335
[[2]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
x 0.6368842 0.3041903 0.08239435 0.5259862 0.3781223 0.6738502 0.9695781
NA 0.3041903 0.08239435 0.5259862 0.3781223 0.6738502 0.9695781
NA 0.6368842 0.30419029 0.3041903 0.5259862 0.5259862 0.6368842
[[2]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
y 1.003443 0.7359498 0.1207160 0.4684568 0.1207160 0.1207160 0.1207160
NA 0.9788402 0.7113473 0.7113473 0.4438543 0.4438543 0.9788402
NA 0.9788402 0.7113473 0.7113473 0.4438543 0.4438543 0.9788402
[[2]]$labs
[1] "0\n\n6388 1030\n100%" "0\n\n6336 302\n89%" "0\n\n6028 55\n82%" "0\n\n308 247\n7%" "0\n\n252 10\n4%" "1\n\n56 237\n4%"
[7] "1\n\n52 728\n11%"
[[2]]$cex
[1] 0.625
[[2]]$boxes
[[2]]$boxes$x1
[1] 0.6259984 0.2933044 0.0715085 0.5151004 0.3672364 0.6629643 0.9586923
[[2]]$boxes$y1
[1] 0.95423774 0.68674479 0.07151102 0.41925185 0.07151102 0.07151102 0.07151102
[[2]]$boxes$x2
[1] 0.64777004 0.31507613 0.09328019 0.53687207 0.38900811 0.68473603 0.98046395
[[2]]$boxes$y2
[1] 1.0034427 0.7359498 0.1207160 0.4684568 0.1207160 0.1207160 0.1207160
[[2]]$split.labs
[1] ""
[[2]]$split.cex
[1] 1 1 1 1 1 1 1
[[2]]$split.box
[[2]]$split.box$x1
[1] 0.22562560 -0.01047745 NA 0.29271505 NA NA NA
[[2]]$split.box$y1
[1] 0.8136345 0.5461416 NA 0.2786486 NA NA NA
[[2]]$split.box$x2
[1] 0.3827550 0.1752661 NA 0.4635295 NA NA NA
[[2]]$split.box$y2
[1] 0.8628395 0.5953465 NA 0.3278536 NA NA NA
[[3]]
[[3]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 1266 0 (0.829334052 0.170665948)
2) mass_sini_na=NA 6011 138 0 (0.977042090 0.022957910)
4) omega_na=NA 5264 27 0 (0.994870821 0.005129179) *
5) omega_na=!NA 747 111 0 (0.851405622 0.148594378)
10) radius_na=!NA 598 16 0 (0.973244147 0.026755853) *
11) radius_na=NA 149 54 1 (0.362416107 0.637583893)
22) k_na=NA 59 13 0 (0.779661017 0.220338983) *
23) k_na=!NA 90 8 1 (0.088888889 0.911111111) *
3) mass_sini_na=!NA 1407 279 1 (0.198294243 0.801705757)
6) impact_parameter_na=!NA 115 6 0 (0.947826087 0.052173913) *
7) impact_parameter_na=NA 1292 170 1 (0.131578947 0.868421053)
14) eccentricity_na=NA 132 47 0 (0.643939394 0.356060606)
28) k_na=NA 89 12 0 (0.865168539 0.134831461) *
29) k_na=!NA 43 8 1 (0.186046512 0.813953488) *
15) eccentricity_na=!NA 1160 85 1 (0.073275862 0.926724138)
30) mag_v_na=NA 117 38 1 (0.324786325 0.675213675)
60) k_na=NA 45 9 0 (0.800000000 0.200000000) *
61) k_na=!NA 72 2 1 (0.027777778 0.972222222) *
31) mag_v_na=!NA 1043 47 1 (0.045062320 0.954937680) *
[[3]]$snipped.nodes
NULL
[[3]]$xlim
[1] 0 1
[[3]]$ylim
[1] 0 1
[[3]]$x
[1] 0.36568827 0.12518313 0.03293459 0.21743168 0.13836150 0.29650186 0.24378841 0.34921532 0.60619342 0.45464223 0.75774460 0.61278260 0.56006914 0.66549605
[15] 0.90270660 0.82363642 0.77092296 0.87634987 0.98177679
[[3]]$y
[1] 0.95519506 0.78655791 0.02769075 0.61792077 0.02769075 0.44928362 0.02769075 0.02769075 0.78655791 0.02769075 0.61792077 0.44928362 0.02769075 0.02769075
[15] 0.44928362 0.28064647 0.02769075 0.02769075 0.02769075
[[3]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
x 0.3656883 0.1251831 0.03293459 0.2174317 0.1383615 0.2965019 0.2437884 0.3492153 0.6061934 0.4546422 0.7577446 0.6127826 0.5600691 0.6654961 0.9027066
NA 0.1251831 0.03293459 0.2174317 0.1383615 0.2965019 0.2437884 0.3492153 0.6061934 0.4546422 0.7577446 0.6127826 0.5600691 0.6654961 0.9027066
NA 0.3656883 0.12518313 0.1251831 0.2174317 0.2174317 0.2965019 0.2965019 0.3656883 0.6061934 0.6061934 0.7577446 0.6127826 0.6127826 0.7577446
[,16] [,17] [,18] [,19]
x 0.8236364 0.7709230 0.8763499 0.9817768
0.8236364 0.7709230 0.8763499 0.9817768
0.9027066 0.8236364 0.8236364 0.9027066
[[3]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
y 0.9998117 0.8311745 0.07230737 0.6625374 0.07230737 0.4939002 0.07230737 0.07230737 0.8311745 0.07230737 0.6625374 0.4939002 0.07230737 0.07230737 0.4939002
NA 0.9850502 0.81641303 0.8164130 0.64777589 0.6477759 0.47913874 0.47913874 0.9850502 0.81641303 0.8164130 0.6477759 0.47913874 0.47913874 0.6477759
NA 0.9850502 0.81641303 0.8164130 0.64777589 0.6477759 0.47913874 0.47913874 0.9850502 0.81641303 0.8164130 0.6477759 0.47913874 0.47913874 0.6477759
[,16] [,17] [,18] [,19]
y 0.3252631 0.07230737 0.07230737 0.07230737
0.4791387 0.31050160 0.31050160 0.47913874
0.4791387 0.31050160 0.31050160 0.47913874
[[3]]$labs
[1] "0\n\n6152 1266\n100%" "0\n\n5873 138\n81%" "0\n\n5237 27\n71%" "0\n\n636 111\n10%" "0\n\n582 16\n8%" "1\n\n54 95\n2%"
[7] "0\n\n46 13\n1%" "1\n\n8 82\n1%" "1\n\n279 1128\n19%" "0\n\n109 6\n2%" "1\n\n170 1122\n17%" "0\n\n85 47\n2%"
[13] "0\n\n77 12\n1%" "1\n\n8 35\n1%" "1\n\n85 1075\n16%" "1\n\n38 79\n2%" "0\n\n36 9\n1%" "1\n\n2 70\n1%"
[19] "1\n\n47 996\n14%"
[[3]]$cex
[1] 0.3625
[[3]]$boxes
[[3]]$boxes$x1
[1] 0.35977882 0.11927368 0.02702513 0.21152222 0.13245204 0.29059241 0.23787895 0.34330586 0.60028396 0.44873277 0.75183514 0.60687314 0.55415968 0.65958659
[15] 0.89679714 0.81772696 0.76501351 0.87044042 0.97586733
[[3]]$boxes$y1
[1] 0.97028869 0.80165154 0.04278438 0.63301439 0.04278438 0.46437725 0.04278438 0.04278438 0.80165154 0.04278438 0.63301439 0.46437725 0.04278438 0.04278438
[15] 0.46437725 0.29574010 0.04278438 0.04278438 0.04278438
[[3]]$boxes$x2
[1] 0.37159773 0.13109259 0.03884404 0.22334114 0.14427096 0.30241132 0.24969787 0.35512478 0.61210287 0.46055169 0.76365406 0.61869205 0.56597860 0.67140551
[15] 0.90861606 0.82954588 0.77683242 0.88225933 0.98768624
[[3]]$boxes$y2
[1] 0.99981168 0.83117453 0.07230737 0.66253738 0.07230737 0.49390024 0.07230737 0.07230737 0.83117453 0.07230737 0.66253738 0.49390024 0.07230737 0.07230737
[15] 0.49390024 0.32526309 0.07230737 0.07230737 0.07230737
[[3]]$split.labs
[1] ""
[[3]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[3]]$split.box
[[3]]$split.box$x1
[1] 0.083630315 -0.001775702 NA 0.103340185 NA 0.221208060 NA NA 0.394116942 NA 0.567186465 0.537488792
[13] NA NA 0.789237154 0.748342614 NA NA NA
[[3]]$split.box$y1
[1] 0.8867017 0.7180646 NA 0.5494274 NA 0.3807903 NA NA 0.7180646 NA 0.5494274 0.3807903 NA NA 0.3807903
[16] 0.2121531 NA NA NA
[[3]]$split.box$x2
[1] 0.16673595 0.06764488 NA 0.17338281 NA 0.26636876 NA NA 0.51516752 NA 0.65837873 0.58264949 NA NA
[15] 0.85803568 0.79350331 NA NA NA
[[3]]$split.box$y2
[1] 0.9162247 0.7475876 NA 0.5789504 NA 0.4103133 NA NA 0.7475876 NA 0.5789504 0.4103133 NA NA 0.4103133
[16] 0.2416761 NA NA NA
[[4]]
[[4]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 19 0 (0.997438663 0.002561337) *
[[4]]$snipped.nodes
NULL
[[4]]$xlim
[1] 0 1
[[4]]$ylim
[1] 0 1
[[4]]$x
[1] 0.5
[[4]]$y
[1] 0.5
[[4]]$branch.x
[,1]
x 0.5
NA
NA
[[4]]$branch.y
[,1]
y 0.5
NA
NA
[[4]]$labs
[1] "0\n\n7399 19\n100%"
[[4]]$cex
[1] 1
[[4]]$boxes
[[4]]$boxes$x1
[1] 0.4819606
[[4]]$boxes$y1
[1] 0.5429313
[[4]]$boxes$x2
[1] 0.5180394
[[4]]$boxes$y2
[1] 0.6191991
[[4]]$split.labs
[1] ""
[[4]]$split.cex
[1] 1
[[4]]$split.box
[[4]]$split.box$x1
[1] NA
[[4]]$split.box$y1
[1] NA
[[4]]$split.box$x2
[1] NA
[[4]]$split.box$y2
[1] NA
[[5]]
[[5]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 101 0 (0.98638447 0.01361553) *
[[5]]$snipped.nodes
NULL
[[5]]$xlim
[1] 0 1
[[5]]$ylim
[1] 0 1
[[5]]$x
[1] 0.5
[[5]]$y
[1] 0.5
[[5]]$branch.x
[,1]
x 0.5
NA
NA
[[5]]$branch.y
[,1]
y 0.5
NA
NA
[[5]]$labs
[1] "0\n\n7317 101\n100%"
[[5]]$cex
[1] 1
[[5]]$boxes
[[5]]$boxes$x1
[1] 0.4819606
[[5]]$boxes$y1
[1] 0.5429313
[[5]]$boxes$x2
[1] 0.5180394
[[5]]$boxes$y2
[1] 0.6191991
[[5]]$split.labs
[1] ""
[[5]]$split.cex
[1] 1
[[5]]$split.box
[[5]]$split.box$x1
[1] NA
[[5]]$split.box$y1
[1] NA
[[5]]$split.box$x2
[1] NA
[[5]]$split.box$y2
[1] NA
[[6]]
[[6]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 2890 1 (0.38959288 0.61040712)
2) radius_na=NA 2444 43 0 (0.98240589 0.01759411) *
3) radius_na=!NA 4974 489 1 (0.09831122 0.90168878)
6) orbital_period_na=NA 347 10 0 (0.97118156 0.02881844) *
7) orbital_period_na=!NA 4627 152 1 (0.03285066 0.96714934) *
[[6]]$snipped.nodes
NULL
[[6]]$xlim
[1] 0 1
[[6]]$ylim
[1] 0 1
[[6]]$x
[1] 0.40180232 0.07651351 0.72709114 0.51023193 0.94395035
[[6]]$y
[1] 0.90176222 0.06208606 0.52009124 0.06208606 0.06208606
[[6]]$branch.x
[,1] [,2] [,3] [,4] [,5]
x 0.4018023 0.07651351 0.7270911 0.5102319 0.9439503
NA 0.07651351 0.7270911 0.5102319 0.9439503
NA 0.40180232 0.4018023 0.7270911 0.7270911
[[6]]$branch.y
[,1] [,2] [,3] [,4] [,5]
y 1.010146 0.1704700 0.6284752 0.1704700 0.1704700
NA 0.9757027 0.9757027 0.5940317 0.5940317
NA 0.9757027 0.9757027 0.5940317 0.5940317
[[6]]$labs
[1] "1\n\n2890 4528\n100%" "0\n\n2401 43\n33%" "1\n\n489 4485\n67%" "0\n\n337 10\n5%" "1\n\n152 4475\n62%"
[[6]]$cex
[1] 0.9148437
[[6]]$boxes
[[6]]$boxes$x1
[1] 0.38562907 0.06034026 0.71091788 0.49405868 0.92777709
[[6]]$boxes$y1
[1] 0.9412592 0.1015830 0.5595882 0.1015830 0.1015830
[[6]]$boxes$x2
[1] 0.41797558 0.09268676 0.74326439 0.52640518 0.96012360
[[6]]$boxes$y2
[1] 1.0101462 0.1704700 0.6284752 0.1704700 0.1704700
[[6]]$split.labs
[1] ""
[[6]]$split.cex
[1] 1 1 1 1 1
[[6]]$split.box
[[6]]$split.box$x1
[1] -0.01169292 NA 0.37848212 NA NA
[[6]]$split.box$y1
[1] 0.7419444 NA 0.3602735 NA NA
[[6]]$split.box$x2
[1] 0.1647199 NA 0.6419817 NA NA
[[6]]$split.box$y2
[1] 0.8108314 NA 0.4291604 NA NA
[[7]]
[[7]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 313 0 (0.957805338 0.042194662)
2) orbital_period_na=!NA 6125 8 0 (0.998693878 0.001306122) *
3) orbital_period_na=NA 1293 305 0 (0.764114462 0.235885538)
6) alternate_names_na=!NA 771 38 0 (0.950713359 0.049286641)
12) star_name_na=NA 562 2 0 (0.996441281 0.003558719) *
13) star_name_na=!NA 209 36 0 (0.827751196 0.172248804)
26) star_sp_type_na=!NA 171 4 0 (0.976608187 0.023391813) *
27) star_sp_type_na=NA 38 6 1 (0.157894737 0.842105263) *
7) alternate_names_na=NA 522 255 1 (0.488505747 0.511494253)
14) semi_major_axis_na=NA 184 31 0 (0.831521739 0.168478261)
28) star_distance_NA=!NA 172 23 0 (0.866279070 0.133720930)
56) star_mass_na=NA 161 15 0 (0.906832298 0.093167702) *
57) star_mass_na=!NA 11 3 1 (0.272727273 0.727272727) *
29) star_distance_NA=NA 12 4 1 (0.333333333 0.666666667) *
15) semi_major_axis_na=!NA 338 102 1 (0.301775148 0.698224852)
30) star_sp_type_na=!NA 113 21 0 (0.814159292 0.185840708)
60) mag_v_na=!NA 70 1 0 (0.985714286 0.014285714) *
61) mag_v_na=NA 43 20 0 (0.534883721 0.465116279)
122) mag_k_na=!NA 11 0 0 (1.000000000 0.000000000) *
123) mag_k_na=NA 32 12 1 (0.375000000 0.625000000) *
31) star_sp_type_na=NA 225 10 1 (0.044444444 0.955555556) *
[[7]]$snipped.nodes
NULL
[[7]]$xlim
[1] 0 1
[[7]]$ylim
[1] 0 1
[[7]]$x
[1] 0.24764414 0.03939514 0.45589314 0.20481278 0.13391951 0.27570606 0.22844388 0.32296825 0.70697350 0.53564808 0.46475480 0.41749262 0.51201699 0.60654136
[15] 0.87829892 0.77195900 0.70106573 0.84285228 0.79559010 0.89011447 0.98463884
[[7]]$y
[1] 0.96299192 0.02254728 0.82050031 0.67800870 0.02254728 0.53551708 0.02254728 0.02254728 0.67800870 0.53551708 0.39302547 0.02254728 0.02254728 0.02254728
[15] 0.53551708 0.39302547 0.02254728 0.25053386 0.02254728 0.02254728 0.02254728
[[7]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
x 0.2476441 0.03939514 0.4558931 0.2048128 0.1339195 0.2757061 0.2284439 0.3229682 0.7069735 0.5356481 0.4647548 0.4174926 0.5120170 0.6065414 0.8782989
NA 0.03939514 0.4558931 0.2048128 0.1339195 0.2757061 0.2284439 0.3229682 0.7069735 0.5356481 0.4647548 0.4174926 0.5120170 0.6065414 0.8782989
NA 0.24764414 0.2476441 0.4558931 0.2048128 0.2048128 0.2757061 0.2757061 0.4558931 0.7069735 0.5356481 0.4647548 0.4647548 0.5356481 0.7069735
[,16] [,17] [,18] [,19] [,20] [,21]
x 0.7719590 0.7010657 0.8428523 0.7955901 0.8901145 0.9846388
0.7719590 0.7010657 0.8428523 0.7955901 0.8901145 0.9846388
0.8782989 0.7719590 0.7719590 0.8428523 0.8428523 0.8782989
[[7]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
y 0.9992683 0.05882365 0.8567767 0.7142851 0.05882365 0.5717935 0.05882365 0.05882365 0.7142851 0.5717935 0.4293018 0.05882365 0.05882365 0.05882365 0.5717935
NA 0.98696705 0.9869670 0.8444754 0.70198382 0.7019838 0.55949221 0.55949221 0.8444754 0.7019838 0.5594922 0.41700060 0.41700060 0.55949221 0.7019838
NA 0.98696705 0.9869670 0.8444754 0.70198382 0.7019838 0.55949221 0.55949221 0.8444754 0.7019838 0.5594922 0.41700060 0.41700060 0.55949221 0.7019838
[,16] [,17] [,18] [,19] [,20] [,21]
y 0.4293018 0.05882365 0.2868102 0.05882365 0.05882365 0.05882365
0.5594922 0.41700060 0.4170006 0.27450899 0.27450899 0.55949221
0.5594922 0.41700060 0.4170006 0.27450899 0.27450899 0.55949221
[[7]]$labs
[1] "0\n\n7105 313\n100%" "0\n\n6117 8\n83%" "0\n\n988 305\n17%" "0\n\n733 38\n10%" "0\n\n560 2\n8%" "0\n\n173 36\n3%"
[7] "0\n\n167 4\n2%" "1\n\n6 32\n1%" "1\n\n255 267\n7%" "0\n\n153 31\n2%" "0\n\n149 23\n2%" "0\n\n146 15\n2%"
[13] "1\n\n3 8\n0%" "1\n\n4 8\n0%" "1\n\n102 236\n5%" "0\n\n92 21\n2%" "0\n\n69 1\n1%" "0\n\n23 20\n1%"
[19] "0\n\n11 0\n0%" "1\n\n12 20\n0%" "1\n\n10 215\n3%"
[[7]]$cex
[1] 0.2875
[[7]]$boxes
[[7]]$boxes$x1
[1] 0.24266775 0.03441875 0.45091676 0.19983640 0.12894312 0.27072968 0.22346749 0.31799186 0.70199711 0.53067169 0.45977842 0.41251623 0.50704060 0.60156497
[15] 0.87332253 0.76698262 0.69608934 0.83787590 0.79061371 0.88513808 0.97966245
[[7]]$boxes$y1
[1] 0.97466580 0.03422116 0.83217419 0.68968258 0.03422116 0.54719097 0.03422116 0.03422116 0.68968258 0.54719097 0.40469935 0.03422116 0.03422116 0.03422116
[15] 0.54719097 0.40469935 0.03422116 0.26220774 0.03422116 0.03422116 0.03422116
[[7]]$boxes$x2
[1] 0.25262053 0.04437152 0.46086953 0.20978917 0.13889589 0.28068245 0.23342026 0.32794463 0.71194989 0.54062446 0.46973119 0.42246900 0.51699337 0.61151774
[15] 0.88327531 0.77693539 0.70604211 0.84782867 0.80056648 0.89509085 0.98961522
[[7]]$boxes$y2
[1] 0.99926829 0.05882365 0.85677668 0.71428507 0.05882365 0.57179346 0.05882365 0.05882365 0.71428507 0.57179346 0.42930184 0.05882365 0.05882365 0.05882365
[15] 0.57179346 0.42930184 0.05882365 0.28681023 0.05882365 0.05882365 0.05882365
[[7]]$split.labs
[1] ""
[[7]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[7]]$split.box
[[7]]$split.box$x1
[1] -0.002282091 NA 0.157537123 0.098462761 NA 0.188010745 NA NA 0.488683441 0.422455525 0.382035870 NA
[13] NA NA 0.731525872 0.672140486 NA 0.766353832 NA NA NA
[[7]]$split.box$y1
[1] 0.9059141 NA 0.7634225 0.6209309 NA 0.4784393 NA NA 0.6209309 0.4784393 0.3359477 NA NA NA 0.4784393
[16] 0.3359477 NA 0.1934561 NA NA NA
[[7]]$split.box$x2
[1] 0.08107237 NA 0.25208845 0.16937625 NA 0.26887701 NA NA 0.58261272 0.50705408 0.45294936 NA NA NA
[15] 0.81239214 0.72999097 NA 0.82482636 NA NA NA
[[7]]$split.box$y2
[1] 0.9305166 NA 0.7880250 0.6455334 NA 0.5030418 NA NA 0.6455334 0.5030418 0.3605502 NA NA NA 0.5030418
[16] 0.3605502 NA 0.2180586 NA NA NA
[[8]]
[[8]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 34 0 (0.995416554 0.004583446)
2) mass_measurement_type_na=NA 4320 6 0 (0.998611111 0.001388889) *
3) mass_measurement_type_na=!NA 3098 28 0 (0.990961911 0.009038089)
6) mag_h_na=NA 2403 7 0 (0.997086975 0.002913025) *
7) mag_h_na=!NA 695 21 0 (0.969784173 0.030215827)
14) semi_major_axis_na=!NA 638 11 0 (0.982758621 0.017241379) *
15) semi_major_axis_na=NA 57 10 0 (0.824561404 0.175438596)
30) tconj_na=!NA 31 0 0 (1.000000000 0.000000000) *
31) tconj_na=NA 26 10 0 (0.615384615 0.384615385)
62) star_sp_type_na=!NA 15 3 0 (0.800000000 0.200000000) *
63) star_sp_type_na=NA 11 4 1 (0.363636364 0.636363636) *
[[8]]$snipped.nodes
NULL
[[8]]$xlim
[1] 0 1
[[8]]$ylim
[1] 0 1
[[8]]$x
[1] 0.24761363 0.06940282 0.42582443 0.25336236 0.59828649 0.43732190 0.75925109 0.62128143 0.89722074 0.80524097 0.98920051
[[8]]$y
[1] 0.95519506 0.02769075 0.78655791 0.02769075 0.61792077 0.02769075 0.44928362 0.02769075 0.28064647 0.02769075 0.02769075
[[8]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
x 0.2476136 0.06940282 0.4258244 0.2533624 0.5982865 0.4373219 0.7592511 0.6212814 0.8972207 0.8052410 0.9892005
NA 0.06940282 0.4258244 0.2533624 0.5982865 0.4373219 0.7592511 0.6212814 0.8972207 0.8052410 0.9892005
NA 0.24761363 0.2476136 0.4258244 0.4258244 0.5982865 0.5982865 0.7592511 0.7592511 0.8972207 0.8972207
[[8]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
y 0.9998117 0.07230737 0.8311745 0.07230737 0.6625374 0.07230737 0.4939002 0.07230737 0.3252631 0.07230737 0.07230737
NA 0.98505018 0.9850502 0.81641303 0.8164130 0.64777589 0.6477759 0.47913874 0.4791387 0.31050160 0.31050160
NA 0.98505018 0.9850502 0.81641303 0.8164130 0.64777589 0.6477759 0.47913874 0.4791387 0.31050160 0.31050160
[[8]]$labs
[1] "0\n\n7384 34\n100%" "0\n\n4314 6\n58%" "0\n\n3070 28\n42%" "0\n\n2396 7\n32%" "0\n\n674 21\n9%" "0\n\n627 11\n9%" "0\n\n47 10\n1%"
[8] "0\n\n31 0\n0%" "0\n\n16 10\n0%" "0\n\n12 3\n0%" "1\n\n4 7\n0%"
[[8]]$cex
[1] 0.3625
[[8]]$boxes
[[8]]$boxes$x1
[1] 0.24170417 0.06349337 0.41991497 0.24745290 0.59237703 0.43141244 0.75334163 0.61537198 0.89131128 0.79933151 0.98329105
[[8]]$boxes$y1
[1] 0.97028869 0.04278438 0.80165154 0.04278438 0.63301439 0.04278438 0.46437725 0.04278438 0.29574010 0.04278438 0.04278438
[[8]]$boxes$x2
[1] 0.25352308 0.07531228 0.43173388 0.25927182 0.60419595 0.44323136 0.76516055 0.62719089 0.90313020 0.81115043 0.99510997
[[8]]$boxes$y2
[1] 0.99981168 0.07230737 0.83117453 0.07230737 0.66253738 0.07230737 0.49390024 0.07230737 0.32526309 0.07230737 0.07230737
[[8]]$split.labs
[1] ""
[[8]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1
[[8]]$split.box
[[8]]$split.box$x1
[1] -0.006051621 NA 0.218963096 NA 0.379906850 NA 0.589059339 NA 0.755912550 NA NA
[[8]]$split.box$y1
[1] 0.8867017 NA 0.7180646 NA 0.5494274 NA 0.3807903 NA 0.2121531 NA NA
[[8]]$split.box$x2
[1] 0.1448573 NA 0.2877616 NA 0.4947369 NA 0.6535035 NA 0.8545694 NA NA
[[8]]$split.box$y2
[1] 0.9162247 NA 0.7475876 NA 0.5789504 NA 0.4103133 NA 0.2416761 NA NA
[[9]]
[[9]]$obj
n= 7418
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 7418 178 0 (0.976004314 0.023995686)
2) star_metallicity_na=!NA 5233 14 0 (0.997324670 0.002675330) *
3) star_metallicity_na=NA 2185 164 0 (0.924942792 0.075057208)
6) mass_sini_na=NA 1873 53 0 (0.971703150 0.028296850)
12) star_sp_type_na=NA 1348 5 0 (0.996290801 0.003709199) *
13) star_sp_type_na=!NA 525 48 0 (0.908571429 0.091428571)
26) star_teff_na=!NA 367 15 0 (0.959128065 0.040871935) *
27) star_teff_na=NA 158 33 0 (0.791139241 0.208860759)
54) orbital_period_na=NA 87 1 0 (0.988505747 0.011494253) *
55) orbital_period_na=!NA 71 32 0 (0.549295775 0.450704225)
110) mass_measurement_type_na=!NA 36 8 0 (0.777777778 0.222222222) *
111) mass_measurement_type_na=NA 35 11 1 (0.314285714 0.685714286)
222) alternate_names_na=!NA 10 4 0 (0.600000000 0.400000000) *
223) alternate_names_na=NA 25 5 1 (0.200000000 0.800000000) *
7) mass_sini_na=!NA 312 111 0 (0.644230769 0.355769231)
14) k_na=!NA 157 3 0 (0.980891720 0.019108280) *
15) k_na=NA 155 47 1 (0.303225806 0.696774194)
30) mag_v_na=!NA 60 24 0 (0.600000000 0.400000000)
60) angular_distance_na=!NA 15 0 0 (1.000000000 0.000000000) *
61) angular_distance_na=NA 45 21 1 (0.466666667 0.533333333)
122) star_mass_na=NA 11 3 0 (0.727272727 0.272727273) *
123) star_mass_na=!NA 34 13 1 (0.382352941 0.617647059) *
31) mag_v_na=NA 95 11 1 (0.115789474 0.884210526) *
[[9]]$snipped.nodes
NULL
[[9]]$xlim
[1] 0 1
[[9]]$ylim
[1] 0 1
[[9]]$x
[1] 0.26411819 0.03982084 0.48841554 0.20955937 0.12603724 0.29308151 0.21225364 0.37390939 0.29847004 0.44934873 0.38468644 0.51401103 0.47090283 0.55711923
[15] 0.76727171 0.64333563 0.89120778 0.79421433 0.72955203 0.85887663 0.81576843 0.90198483 0.98820123
[[9]]$y
[1] 0.96628805 0.02062501 0.84347467 0.72066129 0.02062501 0.59784791 0.02062501 0.47503452 0.02062501 0.35222114 0.02062501 0.22940776 0.02062501 0.02062501
[15] 0.72066129 0.02062501 0.59784791 0.47503452 0.02062501 0.35222114 0.02062501 0.02062501 0.02062501
[[9]]$branch.x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
x 0.2641182 0.03982084 0.4884155 0.2095594 0.1260372 0.2930815 0.2122536 0.3739094 0.2984700 0.4493487 0.3846864 0.5140110 0.4709028 0.5571192 0.7672717
NA 0.03982084 0.4884155 0.2095594 0.1260372 0.2930815 0.2122536 0.3739094 0.2984700 0.4493487 0.3846864 0.5140110 0.4709028 0.5571192 0.7672717
NA 0.26411819 0.2641182 0.4884155 0.2095594 0.2095594 0.2930815 0.2930815 0.3739094 0.3739094 0.4493487 0.4493487 0.5140110 0.5140110 0.4884155
[,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23]
x 0.6433356 0.8912078 0.7942143 0.7295520 0.8588766 0.8157684 0.9019848 0.9882012
0.6433356 0.8912078 0.7942143 0.7295520 0.8588766 0.8157684 0.9019848 0.9882012
0.7672717 0.7672717 0.8912078 0.7942143 0.7942143 0.8588766 0.8588766 0.8912078
[[9]]$branch.y
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15]
y 0.9991693 0.05350624 0.8763559 0.7535425 0.05350624 0.6307291 0.05350624 0.5079158 0.05350624 0.3851024 0.05350624 0.2622890 0.05350624 0.05350624 0.7535425
NA 0.98809816 0.9880982 0.8652848 0.74247139 0.7424714 0.61965801 0.6196580 0.49684463 0.4968446 0.37403125 0.3740313 0.25121787 0.25121787 0.8652848
NA 0.98809816 0.9880982 0.8652848 0.74247139 0.7424714 0.61965801 0.6196580 0.49684463 0.4968446 0.37403125 0.3740313 0.25121787 0.25121787 0.8652848
[,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23]
y 0.05350624 0.6307291 0.5079158 0.05350624 0.3851024 0.05350624 0.05350624 0.05350624
0.74247139 0.7424714 0.6196580 0.49684463 0.4968446 0.37403125 0.37403125 0.61965801
0.74247139 0.7424714 0.6196580 0.49684463 0.4968446 0.37403125 0.37403125 0.61965801
[[9]]$labs
[1] "0\n\n7240 178\n100%" "0\n\n5219 14\n71%" "0\n\n2021 164\n29%" "0\n\n1820 53\n25%" "0\n\n1343 5\n18%" "0\n\n477 48\n7%"
[7] "0\n\n352 15\n5%" "0\n\n125 33\n2%" "0\n\n86 1\n1%" "0\n\n39 32\n1%" "0\n\n28 8\n0%" "1\n\n11 24\n0%"
[13] "0\n\n6 4\n0%" "1\n\n5 20\n0%" "0\n\n201 111\n4%" "0\n\n154 3\n2%" "1\n\n47 108\n2%" "0\n\n36 24\n1%"
[19] "0\n\n15 0\n0%" "1\n\n21 24\n1%" "0\n\n8 3\n0%" "1\n\n13 21\n0%" "1\n\n11 84\n1%"
[[9]]$cex
[1] 0.2625
[[9]]$boxes
[[9]]$boxes$x1
[1] 0.2597639 0.0354665 0.4840612 0.2052050 0.1216829 0.2887272 0.2078993 0.3695550 0.2941157 0.4449944 0.3803321 0.5096567 0.4665485 0.5527649 0.7629174
[16] 0.6389813 0.8868534 0.7898600 0.7251977 0.8545223 0.8114141 0.8976305 0.9838469
[[9]]$boxes$y1
[1] 0.9770270 0.0313640 0.8542137 0.7314003 0.0313640 0.6085869 0.0313640 0.4857735 0.0313640 0.3629601 0.0313640 0.2401467 0.0313640 0.0313640 0.7314003
[16] 0.0313640 0.6085869 0.4857735 0.0313640 0.3629601 0.0313640 0.0313640 0.0313640
[[9]]$boxes$x2
[1] 0.26847253 0.04417517 0.49276988 0.21391371 0.13039157 0.29743585 0.21660797 0.37826372 0.30282437 0.45370307 0.38904077 0.51836537 0.47525717 0.56147357
[15] 0.77162605 0.64768997 0.89556212 0.79856867 0.73390637 0.86323097 0.82012277 0.90633917 0.99255557
[[9]]$boxes$y2
[1] 0.99916928 0.05350624 0.87635590 0.75354252 0.05350624 0.63072913 0.05350624 0.50791575 0.05350624 0.38510237 0.05350624 0.26228899 0.05350624 0.05350624
[15] 0.75354252 0.05350624 0.63072913 0.50791575 0.05350624 0.38510237 0.05350624 0.05350624 0.05350624
[[9]]$split.labs
[1] ""
[[9]]$split.cex
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[[9]]$split.box
[[9]]$split.box$x1
[1] -0.002167417 NA 0.176901843 0.088714344 NA 0.181773274 NA 0.259592024 NA 0.323414689 NA 0.426115365
[13] NA NA 0.625296237 NA 0.767155238 0.684453541 NA 0.782488856 NA NA NA
[[9]]$split.box$y1
[1] 0.9149181 NA 0.7921047 0.6692913 NA 0.5464779 NA 0.4236645 NA 0.3008511 NA 0.1780378 NA NA 0.6692913
[16] NA 0.5464779 0.4236645 NA 0.3008511 NA NA NA
[[9]]$split.box$x2
[1] 0.08180909 NA 0.24221690 0.16336013 NA 0.24273400 NA 0.33734805 NA 0.44595818 NA 0.51569030 NA NA
[15] 0.66137503 NA 0.82127343 0.77465053 NA 0.84904801 NA NA NA
[[9]]$split.box$y2
[1] 0.9370603 NA 0.8142469 0.6914335 NA 0.5686201 NA 0.4458068 NA 0.3229934 NA 0.2001800 NA NA 0.6914335
[16] NA 0.5686201 0.4458068 NA 0.3229934 NA NA NA
---
title: "R Notebook"
output: html_notebook
---

```{r}
library(here)
library(tidyverse)
library(conflicted)
# library(easystats)

exoplanets <- read_csv(here("data", "exoplanet_catalog_080325.csv"))
exoplanets
```


```{r}
library(skimr)
skim(exoplanets)
```

```{r}
library(tidymodels)
glimpse(exoplanets)
```



```{r,fig.asp=2}
library(naniar)
gg_miss_var(exoplanets)
```


```{r, fig.width=20, fig.height=10}
library(visdat)
vis_dat(exoplanets)
```


```{r}
names(exoplanets)
```


```{r}
library(janitor)
exoplanets %>% tabyl(planet_status)
```


```{r}
library(data.table)
# options(repr.matrix.max.rows=100)
exoplanets %>% 
  add_prop_miss() %>%
  arrange(prop_miss_all) %>% 
  head(5) %>% 
  data.table::transpose(keep.names="column") -> preview

preview
# preview %>% View()
```

We have a lot of features:
- Planet name
- Mass (M jup)
- Mass*sin(i) (M jup)
  - This describes minimum mass of the planet due to inclination effect
- Radius (Rjup)
- Period (day)
- a / the average distance of the planet and its star
  - it's in AU (astronomical units), which is the standard distance used for these types of things
  - 1 AU is the average distance tween the earth and the sun
- e / eccentry of a planet (between 0 and 1)
  - represenets how much of a circle is the orbit
  - e = 0 means perfect circle, e > 1 means its not bound to the star
- Discovery - year when it was discovered
- update - year it was updated
- 

```{r}
conflicts_prefer(dplyr::filter)
exoplanets %>% 
  filter(name %>% str_like("%TOI-784%"))
```


```{r}
conflicts_prefer(dplyr::filter)
exoplanets %>% 
  filter(discovered == 2023)
```

```{r}
exoplanets %>%
  mutate(
    ra_rad = ra,  # Convert RA to radians
    dec_rad = dec  # Convert Dec to radians
  ) %>% 
  ggplot(aes(x = ra_rad, y = dec_rad, color = dec)) +
  geom_point(size = 0.4) +
  coord_map("aitoff") +  # Apply Aitoff projection
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "none"  # Optionally remove legend
  )
```


```{r}
# check columsn that start with star
exoplanets %>% 
  select(starts_with("star"))
```


```{r}
library(dplyr)
library(plotly)
conflicts_prefer(plotly::layout)
# Create a new column to distinguish Kepler exoplanets
exoplanets_3d <- exoplanets %>%
  mutate(
    ra_rad = ra * pi / 180,   # Convert RA from degrees to radians
    dec_rad = dec * pi / 180, # Convert Dec from degrees to radians
    x = cos(dec_rad) * cos(ra_rad), # Convert to Cartesian coordinates
    y = cos(dec_rad) * sin(ra_rad),
    z = sin(dec_rad),
    color = case_when(  # Create a column for red when kepler, blue otherwise
      str_detect(paste(name, alternate_names), regex("kepler|koi", ignore_case = TRUE)) ~ "Kepler",
      # if it's free floating (star_name is NA)
      star_name %>% is.na() ~ "Free Floating",
      TRUE ~ "Other"
    ),
    hover_text = paste("Name: ", name) # Create custom hover text with the name of the exoplanet
  )

# Define steps for opacity slider
steps <- list(
  list(args = list("marker.opacity", 0.0), label = "0.0", method = "restyle"),
  list(args = list("marker.opacity", 0.1), label = "0.1", method = "restyle"),
  list(args = list("marker.opacity", 0.2), label = "0.2", method = "restyle"),
  list(args = list("marker.opacity", 0.3), label = "0.3", method = "restyle"),
  list(args = list("marker.opacity", 0.4), label = "0.4", method = "restyle"),
  list(args = list("marker.opacity", 0.5), label = "0.5", method = "restyle"),
  list(args = list("marker.opacity", 0.6), label = "0.6", method = "restyle"),
  list(args = list("marker.opacity", 0.7), label = "0.7", method = "restyle"),
  list(args = list("marker.opacity", 0.8), label = "0.8", method = "restyle"),
  list(args = list("marker.opacity", 0.9), label = "0.9", method = "restyle"),
  list(args = list("marker.opacity", 1.0), label = "1.0", method = "restyle")
)

# Create an interactive 3D scatter plot with plotly
plot_ly(
  data = exoplanets_3d,
  x = ~x,
  y = ~y,
  z = ~z,
  color = ~color,  # Use the kepler_highlight column for color mapping
  colors = c("Other" = "red", "Kepler" = "blue", "Free Floating" = "green"),
  text = ~hover_text, # Show the name of the exoplanet on hover
  type = "scatter3d",
  mode = "markers",
  marker = list(size = 1, opacity = 0.7), # Default opacity
  showlegend = TRUE
) %>%
  layout(
    title = "3D Sky Map of Exoplanets (Kepler Highlighted)",
    scene = list(
      xaxis = list(title = "X"),
      yaxis = list(title = "Y"),
      zaxis = list(title = "Z")
    ),
    sliders = list(
      list(
        active = 1,  # Set the default opacity value to 1.0 (fully opaque)
        currentvalue = list(
          prefix = "Opacity: ",
          font = list(size = 15)
        ),
        pad = list(t = 60),
        steps = steps  # Use the steps defined earlier for the opacity slider
      )
    )
  )

```

```{r}

# Assuming your data is loaded as 'exoplanets'
# Convert RA to degrees (if it's in hours:minutes:seconds format)
# If RA is already in degrees, skip this step
exoplanets %>%
  mutate(
    ra_deg = ra,  # Convert RA from hours to degrees (if needed)
    # Convert to polar coordinates for plotting
    # RA is mapped to theta (0-360 degrees)
    theta = ra_deg
  ) %>% 
ggplot(aes(x = theta, y = star_distance, color = mass)) +
  # Use coord_polar for circular plot
  coord_polar(start = 0, direction = -1) + # Start at 0 degrees, clockwise direction
  # Add concentric circles for distance reference
  geom_hline(yintercept = c(10, 100, 1000, 10000), 
             color = "gray", linetype = "solid", size = 0.3, alpha = 0.7) +
  # Add radial lines for angle reference
  geom_vline(xintercept = seq(0, 330, by = 30), 
             color = "gray", linetype = "solid", size = 0.3, alpha = 0.7) +
  # Plot the exoplanets
  geom_point(alpha = 0.8, size = 1) +
  # Use log scale for distance
  scale_y_log10(
    breaks = c(10, 100, 1000, 10000),
    labels = c("10 pc", "100 pc", "1000 pc", "10000 pc"),
    limits = c(1, 15000)
  ) +
  # Use log scale for mass colors
  scale_color_gradientn(
    colors = c("#1E90FF", "#32CD32", "#FFFF00", "#FFA500", "#FF4500", "#FF0000"),
    trans = "log10",
    breaks = c(0.0001, 0.001, 0.01, 0.1, 1, 10),
    labels = c("10⁻⁴", "10⁻³", "10⁻²", "10⁻¹", "10⁰", "10¹"),
    name = "Planetary Mass (MJup)"
  ) +
  # Remove grid and axis elements
  theme_minimal() +
  theme(
    axis.title = element_blank(),
    axis.text.y = element_blank(),
    axis.text.x = element_blank(),
    panel.grid = element_blank(),
    legend.position = "bottom",
    legend.box = "horizontal",
    plot.title = element_text(hjust = 0.5)
  ) +
  ggtitle("Exoplanet Distribution")
```


```{r}
library(dplyr)
library(plotly)

# Create a new column to distinguish Kepler exoplanets
exoplanets_3d <- exoplanets %>%
  mutate(
    ra_rad = ra * pi / 180,   # Convert RA from degrees to radians
    dec_rad = dec * pi / 180, # Convert Dec from degrees to radians
    x = cos(dec_rad) * cos(ra_rad), # Convert to Cartesian coordinates
    y = cos(dec_rad) * sin(ra_rad),
    z = sin(dec_rad),
    color = case_when(  # Create a column for red when kepler, blue otherwise
      str_detect(paste(name, alternate_names), regex("kepler|koi", ignore_case = TRUE)) ~ "Kepler",
      # if it's free floating (star_name is NA)
      star_name %>% is.na() ~ "Free Floating",
      TRUE ~ "Other"
    ),
    hover_text = paste("Name: ", name), # Create custom hover text with the name of the exoplanet
    scaled_x = x * (1 / star_distance),  # Adjust x coordinate by star distance (closer = closer to center)
    scaled_y = y * (1 / star_distance),  # Adjust y coordinate similarly
    scaled_z = z * (1 / star_distance)   # Adjust z coordinate similarly
  )

# Define steps for opacity slider
steps <- list(
  list(args = list("marker.opacity", 0.0), label = "0.0", method = "restyle"),
  list(args = list("marker.opacity", 0.1), label = "0.1", method = "restyle"),
  list(args = list("marker.opacity", 0.2), label = "0.2", method = "restyle"),
  list(args = list("marker.opacity", 0.3), label = "0.3", method = "restyle"),
  list(args = list("marker.opacity", 0.4), label = "0.4", method = "restyle"),
  list(args = list("marker.opacity", 0.5), label = "0.5", method = "restyle"),
  list(args = list("marker.opacity", 0.6), label = "0.6", method = "restyle"),
  list(args = list("marker.opacity", 0.7), label = "0.7", method = "restyle"),
  list(args = list("marker.opacity", 0.8), label = "0.8", method = "restyle"),
  list(args = list("marker.opacity", 0.9), label = "0.9", method = "restyle"),
  list(args = list("marker.opacity", 1.0), label = "1.0", method = "restyle")
)

# Create an interactive 3D scatter plot with plotly
fig <- plot_ly(
  data = exoplanets_3d,
  x = ~scaled_x,
  y = ~scaled_y,
  z = ~scaled_z,
  color = ~color,  # Use the kepler_highlight column for color mapping
  colors = c("Other" = "red", "Kepler" = "blue", "Free Floating" = "green"),
  text = ~hover_text, # Show the name of the exoplanet on hover
  type = "scatter3d",
  mode = "markers",
  marker = list(size = 2, opacity = 0.7), # Default opacity
  showlegend = TRUE
)

# Add layout with a slider for opacity
fig <- fig %>% layout(
  title = "3D Sky Map of Exoplanets (Kepler Highlighted)",
  scene = list(
    xaxis = list(title = "X"),
    yaxis = list(title = "Y"),
    zaxis = list(title = "Z")
  ),
  sliders = list(
    list(
      active = 1,  # Set the default opacity value to 1.0 (fully opaque)
      currentvalue = list(
        prefix = "Opacity: ",
        font = list(size = 15)
      ),
      pad = list(t = 60),
      steps = steps  # Use the steps defined earlier for the opacity slider
    )
  )
)

fig


```



```{r}
exoplanets %>% names()
```

```{r}
# check how many are missing
exoplanets %>% 
  select(ra, dec, angular_distance) %>% 
  mutate(ra = ra %>% is.na(), dec = dec %>% is.na(), angular_distance = angular_distance %>% is.na()) %>%
  summarise_all(mean) %>%
  gather(key="column", value="percentage")
```


```{r}
# check which ones dont have ra
exoplanets %>% 
  filter(ra %>% is.na())
```

```{r}
# check out alternate names
exoplanets %>% 
  select(name, alternate_names) %>% 
  filter(alternate_names %>% str_length() > 0)

```

```{r}
exoplanets %>% 
  tabyl(publication)
```


```{r}
# remove any column with error in the name
exoplanets_r <- exoplanets %>% 
  select(-contains("error")) %>% 
  select(-planet_status, -publication) %>% # useless
  select(-hot_point_lon, ) # too many missings
exoplanets_r %>% names
```

```{r, fig.width=20, fig.height=10}
library(visdat)
vis_dat(exoplanets_r)
```

```{r, fig.width=20, fig.height=10}
vis_miss(exoplanets_r, sort_miss = T, cluster = T)
```


# detection type
```{r}
exoplanets %>% 
  tabyl("detection_type") %>% 
  arrange(-n)
```

```{r}
library(fastDummies)
exoplanets_rd <- exoplanets_r %>% 
  dummy_cols(select_columns = "detection_type", split = ", ")
exoplanets_rd %>% select(starts_with("detection_type")) %>% 
  unique
```


```{r}
exoplanets_rd %>% 
  select(starts_with("detection_type")) %>% 
  gather(key="detection_type", value="value") %>% 
  filter(value == 1) %>% 
  group_by(detection_type) %>% 
  summarise(n = n(), percentage = n()*100 / nrow(exoplanets_rd)) %>% 
  arrange(-n)
```


```{r, fig.width=10, fig.height=20}
library(naniar)
exoplanets_rd %>%
  group_by(`detection_type_Primary Transit`) %>% 
  miss_var_summary() %>% 
  arrange(variable) %>% 
  filter(variable %>% str_detect("detection_type", negate = T)) %>% 
  ggplot(aes(x = variable, y = pct_miss, fill = `detection_type_Primary Transit`)) +
  geom_col(position="dodge") +
  coord_flip() 
```


```{r}
if (F){
library(misty)
exoplanets_rd %>% 
  select(tzero_vr, tzero_tr_sec, tzero_tr) %>% 
  na.test(data = exoplanets_rd)
} # didnt work for some reason
```


```{r}
library(shiny)
library(dplyr)
library(plotly)
library(naniar)  # Assuming miss_var_summary() is from naniar

# Sample UI
ui <- fluidPage(
  titlePanel("Missing Data by Detection Type"),
  
  sidebarLayout(
    sidebarPanel(
      selectInput("group_var", "Select Detection Type:", 
                  choices = names(exoplanets_rd)[grepl("^detection_type_", names(exoplanets_rd))])
    ),
    
    mainPanel(
      plotlyOutput("missing_plot", height = "700px")  # Increased height
    )
  )
)

# Server function
server <- function(input, output) {
  output$missing_plot <- renderPlotly({
    exoplanets_rd %>%
      group_by(.data[[input$group_var]]) %>%
      miss_var_summary() %>%
      arrange(variable) %>%
      filter(!str_detect(variable, "detection_type")) %>%
      plot_ly(y = ~variable, x = ~pct_miss, color = ~.data[[input$group_var]], type = "bar") %>%
      layout(barmode = "group", height = 700)  # Increased plot height
  })
}
# Run the app
if (F) {
  shinyApp(ui = ui, server = server)
}
```


## Kepler

```{r}
# filter by the kepler
exoplanets %>% 
  filter(paste(name, alternate_names) %>% str_like("%Kepler%")) %>% 
  tabyl("detection_type")
```

```{r}
# check other
exoplanets %>% 
  filter(detection_type == "Other")
```

```{r}
exoplanets_rd %>% 
  select(name, star_distance, star_name)
```


```{r}
conflicts_prefer(lubridate::yday)
conflicts_prefer(lubridate::year)
year_with_percentage <- function(date) {
  percentage_of_year <- yday(date) / ifelse(leap_year(date), 366, 365)
  year(date) + percentage_of_year
}

exoplanets_rd %>% 
  mutate(updated = updated %>% year_with_percentage) %>% 
  mutate(diff_disc_updated = updated - discovered) -> exoplanets_rdd
exoplanets_rdd %>% 
  select(discovered, updated, diff_disc_updated)
```

```{r}
exoplanets_rddk <- exoplanets_rdd %>% 
  mutate(is_kepler = paste(name, alternate_names) %>% str_detect("kepler" %>% regex(ignore_case = T)))
exoplanets_rddk %>%
  select(name, is_kepler) %>% 
  arrange(-is_kepler)
```



```{r}
exoplanets %>%
  filter(publication %>% is_na)
```


# Modeling

```{r}
# transform into is shadow matrix
library(naniar)
exoplanets_rddk %>% 
  select(-name, -discovered, -updated, -diff_disc_updated, -is_kepler, -star_distance, -starts_with("detection_type")) %>%
  janitor::remove_constant() %>%
  as_shadow() -> shadow_matrix
# add columns to exoplanets_rd
shadow_exoplanets <- exoplanets_rddk %>% 
  bind_cols(shadow_matrix) %>% 
  # select everyone that ends with _NA
  select(name, starts_with("detection_type_"), discovered, updated, diff_disc_updated, is_kepler, star_distance, ra, dec, ends_with("_NA")) %>% 
  # change detection_type to factor
  mutate_at(vars(starts_with("detection_type_")), as.factor) %>% 
  janitor::clean_names()
# TODO reduce dimensionality on the _NA 
shadow_exoplanets
```

# clustering
TODO

# model

```{r}
shadow_exoplanets %>% glimpse
```


```{r}
library(rpart)
library(dplyr)
library(purrr)

set.seed(123)

# Define target and predictor columns
target_cols <- names(shadow_exoplanets) %>% 
  keep(~ startsWith(.x, "detection_type_"))

predictor_cols <- names(shadow_exoplanets) %>% 
  setdiff(c("name", target_cols))

# Train decision trees for each target label
models <- target_cols %>%
  set_names() %>%
  map(~ rpart(as.formula(paste(.x, "~", paste(predictor_cols, collapse = " + "))),
              data = shadow_exoplanets, method = "class"))

# Make predictions and add them to the original dataset
shadow_exoplanets_with_preds <- shadow_exoplanets %>%
  bind_cols(models %>%
    map_dfc(~ predict(.x, shadow_exoplanets, type = "class")) %>%
    rename_with(~ paste0("pred_", target_cols))  # Prefix predictions for clarity
  )

predictions <- shadow_exoplanets_with_preds %>%
  mutate(
    actual_combined = apply(select(., all_of(target_cols)), 1, paste, collapse = "_"),
    predicted_combined = apply(select(., starts_with("pred_")), 1, paste, collapse = "_")
  ) %>% select(actual_combined, predicted_combined, starts_with("pred_"), starts_with("detection_type_"))
predictions
```

```{r}
multi_label_confusion_matrix <- function(y_true, y_pred) {
  result <- list()
  
  for (col in names(y_true)) {
    confusion_matrix <- table(y_true[[col]], y_pred[[paste0("pred_", col)]])
    result[[col]] <- confusion_matrix
  }
  
  return(result)
}
multi_label_confusion_matrix(shadow_exoplanets_with_preds %>% select(starts_with("detection_type_")), predictions %>% select(starts_with("pred_")))
```

```{r}
# Load necessary library
library(rpart.plot)

# Plot the decision trees with titles
target_cols %>%
  map2(models, ~ {
    rpart.plot(.y, 
               type = 4, 
               extra = 101, 
               under = TRUE, 
               fallen.leaves = TRUE,
               main = paste("Decision Tree for", .x))  # Title with the target label
  })

```

```{r}
# get accuracy
accuracy <- function(y_true, y_pred) {
  result <- list()
  
  for (col in names(y_true)) {
    confusion_matrix <- table(y_true[[col]], y_pred[[paste0("pred_", col)]])
    result[[col]] <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
  }
  
  return(result)
}

accuracy(shadow_exoplanets_with_preds %>% select(starts_with("detection_type_")), predictions %>% select(starts_with("pred_")))
```

```{r}
set.seed(123)

shadow_exoplanets %>% 
  select(-name, -starts_with("detection_type"), -ends_with("_na"), -discovered, -updated, -diff_disc_updated, -is_kepler) %>% 
  as_shadow() %>% 
  bind_cols(shadow_exoplanets) %>% 
  select(-discovered, -updated, -diff_disc_updated, -is_kepler, -star_distance, -ra, -dec) %>%
  relocate(name, starts_with("detection_type")) -> shadower_exoplanets

shadower_exoplanets %>% glimpse
```


```{r}
# Do the same thing with shadower_exoplanets


# Define target and predictor columns
target_cols <- names(shadower_exoplanets) %>% 
  keep(~ startsWith(.x, "detection_type_"))

predictor_cols <- names(shadower_exoplanets) %>%
  setdiff(c("name", target_cols))

# Train decision trees for each target label
models <- target_cols %>%
  set_names() %>%
  map(~ rpart(as.formula(paste(.x, "~", paste(predictor_cols, collapse = " + "))),
              data = shadower_exoplanets, method = "class"))

# Make predictions and add them to the original dataset
shadower_exoplanets_with_preds <- shadower_exoplanets %>%
  bind_cols(models %>%
    map_dfc(~ predict(.x, shadower_exoplanets, type = "class")) %>%
    rename_with(~ paste0("pred_", target_cols))  # Prefix predictions for clarity
  )

predictions <- shadower_exoplanets_with_preds %>%
  mutate(
    actual_combined = apply(select(., all_of(target_cols)), 1, paste, collapse = "_"),
    predicted_combined = apply(select(., starts_with("pred_")), 1, paste, collapse = "_")
  ) %>% select(actual_combined, predicted_combined, starts_with("pred_"), starts_with("detection_type_"))
predictions
```


```{r}
# accuracy
accuracy(shadower_exoplanets_with_preds %>% select(starts_with("detection_type_")), predictions %>% select(starts_with("pred_")))
```

```{r}
# plot
target_cols %>%
  map2(models, ~ {
    rpart.plot(.y, 
               type = 4, 
               extra = 101, 
               under = TRUE, 
               fallen.leaves = TRUE,
               main = paste("Decision Tree for", .x))  # Title with the target label
  })
```

